Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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EB-Manipulation Dataset

EB-Manipulation is a benchmark for evaluating LLM-controlled robotic manipulation in CoppeliaSim using a Franka Panda arm with a parallel gripper. It is part of the EmbodiedBench benchmark suite, designed for use with the EASI evaluation framework.

Dataset Description

Agents must output sequences of 7D discrete gripper actions [X, Y, Z, Roll, Pitch, Yaw, Gripper] to complete manipulation tasks (picking, stacking, placing, wiping). The benchmark tests spatial reasoning, visual understanding, common sense, and complex instruction following.

Subsets

Subset	Description	Episodes
`base`	Standard manipulation tasks with direct instructions	48
`common_sense`	Tasks requiring commonsense reasoning about objects	48
`complex`	Complex multi-step manipulation instructions	48
`spatial`	Tasks with relative spatial references	48
`visual`	Tasks requiring visual property recognition	36

Task Types

Task Type	Base Task	Description
`pick`	pick_cube	Pick up a target object and place it into a container
`stack`	stack_cubes	Stack cubes in a specified order
`place`	place_into_shape_sorter	Place objects into the correct shape sorter slots
`wipe`	wipe_table	Wipe a specified area on the table

Action Space

Each action is a 7D discrete array: [X, Y, Z, Roll, Pitch, Yaw, Gripper_state]

X, Y, Z: 3D position in voxel grid (range [0, 100])
Roll, Pitch, Yaw: Discrete Euler angles (range [0, 120], each unit = 3 degrees)
Gripper state: 0 = close, 1 = open

Dataset Structure

.
├── data/
│   ├── base.jsonl
│   ├── common_sense.jsonl
│   ├── complex.jsonl
│   ├── spatial.jsonl
│   └── visual.jsonl
├── simulator_data.zip          # Binary simulation files (auto-extracted by EASI)
│   ├── data/                   # Per-split episode data (.ttm, .pkl)
│   ├── vlm/                    # Task templates and object models
│   └── amsolver/robot_ttms/    # Robot model files
└── README.md

Data Fields (JSONL)

Each row in the JSONL files contains:

id (int): Unique identifier within the split
task_name (string): Task variation name (e.g., pick_cube_shape)
variation (int): Variation number within the task
episode_num (int): Episode number within the variation
instruction (string): Natural language task instruction
task_type (string): Base task type (pick, stack, place, wipe)

Simulator Data (simulator_data.zip)

Each episode's binary data is stored at: data/{split}/eval/{task_name}/variation{N}/episodes/episode{N}/

task_base.ttm — CoppeliaSim scene state
waypoint_sets.ttm — Waypoint configuration
configs.pkl — Episode metadata and success conditions

Usage

Loading with Datasets Library

from datasets import load_dataset

# Load a specific split
dataset = load_dataset("oscarqjh/EB-Manipulation_easi", split="base")

# Access data
for example in dataset:
    print(example["instruction"])
    print(example["task_name"])

Using with EASI

# Run evaluation on the base split
easi run ebmanipulation_base --agent react --backend openai --model gpt-4o

# List available manipulation splits
easi task list | grep ebmanipulation

Requirements

CoppeliaSim V4.1.0
PyRep (CoppeliaSim Python binding)
AMSolver (modified RLBench fork)

Acknowledgements

This dataset is derived from the EmbodiedBench EB-Manipulation benchmark and uses CoppeliaSim as the simulation environment.

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