| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | tags: |
| | - in-context-reinforcement-learning |
| | - reinforcement-learning |
| | - in-context-learning |
| | - metaworld |
| | - mujoco |
| | - bi-dexhands |
| | - industrial-benchmark |
| | model-index: |
| | - name: dunnolab/Vintix |
| | results: |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: MuJoCo |
| | type: mujoco |
| | metrics: |
| | - type: iqm_normalized_95 |
| | value: 0.99 |
| | name: Normalized Score IQM (95% CI) |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: Meta-World |
| | type: metaworld |
| | metrics: |
| | - type: iqm_normalized_95 |
| | value: 0.99 |
| | name: Normalized Score IQM (95% CI) |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: Bi-DexHands |
| | type: bi-dexhands |
| | metrics: |
| | - type: iqm_normalized_95 |
| | value: 0.92 |
| | name: Normalized Score IQM (95% CI) |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: Industrial-Benchmark |
| | type: industrial-benchmark |
| | metrics: |
| | - type: iqm_normalized_95 |
| | value: 0.99 |
| | name: Normalized Score IQM (95% CI) |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: ant_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: 6315.00 +/- 675.00 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.98 +/- 0.10 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: halfcheetah_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: 7226.50 +/- 241.50 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.93 +/- 0.03 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: hopper_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: 2794.60 +/- 612.62 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.86 +/- 0.19 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: humanoid_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: 7376.26 +/- 0.00 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.00 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: humanoidstandup_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: 320567.82 +/- 58462.11 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.02 +/- 0.21 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: inverteddoublependulum_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: 6105.75 +/- 4368.65 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.65 +/- 0.47 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: invertedpendulum_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: 1000.00 +/- 0.00 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.00 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: pusher_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: -37.82 +/- 8.72 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.02 +/- 0.08 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: reacher_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: -6.25 +/- 2.63 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.98 +/- 0.07 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: swimmer_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: 93.20 +/- 5.40 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.98 +/- 0.06 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: walker2d_v4 |
| | type: MuJoCo |
| | metrics: |
| | - type: total_reward |
| | value: 5400.00 +/- 107.95 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: assembly-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 307.08 +/- 25.20 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.04 +/- 0.10 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: basketball-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 568.04 +/- 60.72 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.02 +/- 0.11 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: bin-picking-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 7.88 +/- 4.28 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.01 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: box-close-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 61.75 +/- 13.54 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: -0.04 +/- 0.03 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: button-press-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 624.67 +/- 42.77 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.07 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: button-press-topdown-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 449.36 +/- 62.16 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.94 +/- 0.14 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: button-press-topdown-wall-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 482.08 +/- 32.48 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.07 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: button-press-wall-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 672.00 +/- 26.48 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.04 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: coffee-button-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 719.00 +/- 41.10 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.06 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: coffee-pull-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 26.04 +/- 56.12 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.07 +/- 0.20 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: coffee-push-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 571.01 +/- 112.28 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.01 +/- 0.20 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: dial-turn-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 783.90 +/- 53.17 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.07 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: disassemble-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 523.60 +/- 58.15 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.12 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: door-close-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 538.10 +/- 25.76 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.02 +/- 0.05 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: door-lock-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 356.51 +/- 249.44 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.35 +/- 0.36 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: door-open-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 581.33 +/- 26.33 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.05 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: door-unlock-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 352.86 +/- 147.78 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.21 +/- 0.26 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: drawer-close-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 838.88 +/- 7.41 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.96 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: drawer-open-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 493.00 +/- 3.57 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: faucet-close-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 749.46 +/- 14.83 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.03 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: faucet-open-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 732.47 +/- 15.23 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.03 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: hammer-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 669.31 +/- 69.56 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.12 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: hand-insert-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 142.81 +/- 146.64 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.19 +/- 0.20 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: handle-press-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 835.30 +/- 114.19 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.15 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: handle-press-side-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 852.96 +/- 16.08 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: handle-pull-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 701.10 +/- 13.82 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: handle-pull-side-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 493.10 +/- 53.65 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.11 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: lever-pull-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 548.72 +/- 81.12 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.96 +/- 0.16 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: peg-insert-side-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 352.43 +/- 137.24 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.01 +/- 0.40 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: peg-unplug-side-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 401.52 +/- 175.27 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.75 +/- 0.34 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: pick-out-of-hole-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 364.20 +/- 79.56 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.91 +/- 0.20 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: pick-place-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 414.02 +/- 91.10 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.98 +/- 0.22 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: pick-place-wall-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 553.18 +/- 84.72 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.04 +/- 0.16 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: plate-slide-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 531.98 +/- 156.94 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.34 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: plate-slide-back-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 703.93 +/- 108.27 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.16 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: plate-slide-back-side-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 721.29 +/- 62.15 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.09 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: plate-slide-side-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 578.24 +/- 143.73 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.83 +/- 0.22 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: push-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 729.33 +/- 104.40 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.14 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: push-back-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 372.16 +/- 112.75 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.95 +/- 0.29 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: push-wall-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 741.68 +/- 14.84 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: reach-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 684.45 +/- 136.55 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.01 +/- 0.26 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: reach-wall-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 738.02 +/- 100.96 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.98 +/- 0.17 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shelf-place-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 268.34 +/- 29.07 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.01 +/- 0.11 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: soccer-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 438.44 +/- 189.63 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.80 +/- 0.35 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: stick-pull-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 483.98 +/- 83.25 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.92 +/- 0.16 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: stick-push-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 563.07 +/- 173.40 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.90 +/- 0.28 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: sweep-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 487.19 +/- 60.02 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.94 +/- 0.12 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: sweep-into-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 798.80 +/- 15.62 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: window-close-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 562.48 +/- 91.17 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.95 +/- 0.17 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: window-open-v2 |
| | type: Meta-World |
| | metrics: |
| | - type: total_reward |
| | value: 573.69 +/- 93.98 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.96 +/- 0.17 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandblockstack |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 347.40 +/- 50.60 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.17 +/- 0.23 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandbottlecap |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 338.25 +/- 81.25 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.81 +/- 0.25 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandcatchabreast |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 11.81 +/- 21.28 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.17 +/- 0.32 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandcatchover2underarm |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 31.60 +/- 7.20 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.92 +/- 0.24 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandcatchunderarm |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 18.21 +/- 9.46 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.72 +/- 0.39 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhanddoorcloseinward |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 3.97 +/- 0.15 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.36 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhanddoorcloseoutward |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 358.50 +/- 4.50 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: -1.27 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhanddooropeninward |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 108.25 +/- 8.50 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.29 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhanddooropenoutward |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 83.65 +/- 12.10 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.13 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandgraspandplace |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 485.15 +/- 89.10 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.18 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandkettle |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: -450.47 +/- 0.00 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: -0.99 +/- 0.00 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandliftunderarm |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 377.92 +/- 13.24 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.95 +/- 0.03 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandover |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 33.01 +/- 0.96 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.95 +/- 0.03 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandpen |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 98.80 +/- 83.60 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.52 +/- 0.44 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandpushblock |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 445.60 +/- 2.20 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.98 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandreorientation |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 2798.00 +/- 2112.00 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.89 +/- 0.66 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandscissors |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 747.95 +/- 7.65 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.03 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandswingcup |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 3775.50 +/- 583.70 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.95 +/- 0.13 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandswitch |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 268.25 +/- 2.35 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.95 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: shadowhandtwocatchunderarm |
| | type: Bi-DexHands |
| | metrics: |
| | - type: total_reward |
| | value: 2.17 +/- 0.67 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.03 +/- 0.03 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-0-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -191.39 +/- 22.96 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.94 +/- 0.13 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-5-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -194.01 +/- 3.66 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-10-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -213.28 +/- 2.01 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.01 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-15-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -227.82 +/- 4.29 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.01 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-20-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -259.99 +/- 22.70 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.95 +/- 0.11 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-25-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -282.28 +/- 20.70 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.95 +/- 0.11 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-30-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -307.02 +/- 19.23 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.90 +/- 0.10 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-35-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -314.36 +/- 5.62 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 1.00 +/- 0.03 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-40-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -339.34 +/- 9.57 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.05 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-45-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -366.63 +/- 7.47 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.04 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-50-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -395.94 +/- 17.65 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.91 +/- 0.09 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-55-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -403.73 +/- 2.03 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-60-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -434.25 +/- 4.12 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.98 +/- 0.02 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-65-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -480.31 +/- 8.63 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.86 +/- 0.04 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-70-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -480.76 +/- 5.98 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.95 +/- 0.03 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-75-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -476.83 +/- 2.44 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.99 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-80-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -497.13 +/- 2.95 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.96 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-85-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -513.83 +/- 3.06 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.98 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-90-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -532.70 +/- 3.61 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-95-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -557.42 +/- 3.81 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.01 |
| | name: Expert normalized total reward |
| | - task: |
| | type: in-context-reinforcement-learning |
| | name: In-Context Reinforcement Learning |
| | dataset: |
| | name: industrial-benchmark-100-v1 |
| | type: Industrial-Benchmark |
| | metrics: |
| | - type: total_reward |
| | value: -574.57 +/- 4.37 |
| | name: Total reward |
| | - type: normalized_total_reward |
| | value: 0.97 +/- 0.01 |
| | name: Expert normalized total reward |
| | --- |
| | # Model Card for Vintix |
| |
|
| | This is a multi-task action model via in-context reinforcement learning |
| |
|
| | ## Model Details |
| | | Setting | Description | |
| | |---------------------------------|----------------------------------------------------------------| |
| | | Parameters | 332M | |
| | | Model Sizes | Layers: 20, Heads: 16, Embedding Size: 1024 | |
| | | Sequence Length | 8192 | |
| | | Training Data | MuJoCo, Meta-World, Bi-DexHands, Industrial Benchmark | |
| |
|
| | ### Model Description |
| | - Developed by: [dunnolab](https://dunnolab.ai) |
| | - License: Apache 2.0 |
| |
|
| | ### Model Sources |
| | - Repository: https://github.com/dunnolab/vintix |
| | - Paper: https://arxiv.org/abs/2501.19400 |
| |
|
| | ## Citation |
| |
|
| | ```bibex |
| | @article{polubarov2025vintix, |
| | author={Andrey Polubarov and Nikita Lyubaykin and Alexander Derevyagin and Ilya Zisman and Denis Tarasov and Alexander Nikulin and Vladislav Kurenkov}, |
| | title={Vintix: Action Model via In-Context Reinforcement Learning}, |
| | journal={arXiv}, |
| | volume={2501.19400}, |
| | year={2025} |
| | } |
| | ``` |
