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GlobMP / applications /avoiding /avoiding_baselines /DiRecT /eval_all.sbatch
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#!/bin/bash
#SBATCH --job-name=eval_all
#SBATCH --partition=<your-partition>
#SBATCH --nodelist=<your-node>
#SBATCH --gpus=1
#SBATCH --cpus-per-task=32
#SBATCH --ntasks=1
#SBATCH --time=72:00:00
#SBATCH --mem=64G
#SBATCH --output=slurm_logs/%j_%x.out
#SBATCH --error=slurm_logs/%j_%x.err
mkdir -p slurm_logs
source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate direct-d3il
cd "$(dirname "$0")"
echo "============================================"
echo "Eval All Methods (dynamics always on) - $(date)"
echo "============================================"
# ============================================================
# Shared parameters
# ============================================================
CP=10 # Diffusion checkpoint
EXP=x0pred
SEED=0
REPEAT=100
BATCH=1
STEPS=32 # Sampling freq.
STEPS_LONG=256
REPLAN=4 # Replan freq.
MARGIN=0.025
RELAXATION=0 # No relaxation (equality constraints)
KP=0.0 # No pdi tracker (open loop over executed horizon)
KD=0.0
KI=0.0
COMMON="--diffusion-exp-name $EXP --diffusion-cp $CP --seed $SEED \
--random-repeat $REPEAT --batch-size $BATCH --replan-steps $REPLAN \
--n-sampling-steps $STEPS --obstacle-margin $MARGIN \
--pd-kp $KP --pd-kd $KD --pd-ki $KI --no-early-stop"
# ============================================================
# 1. Unconditional (baseline)
# ============================================================
echo "=== Unconditional ==="
python eval.py $COMMON \
--guidance-method no \
--exp-name uncond
# ============================================================
# 2. Gradient Guidance (DPS)
# ============================================================
echo "=== Gradient Guidance ==="
python eval.py $COMMON \
--guidance-method gradient \
--constraints \
--guidance-lr 0.05 \
--guidance-steps 5 \
--gradient-start 0.0 \
--obstacle-weight 100.0 \
--dynamics-weight 10.0 \
--exp-name gradient
# ============================================================
# 3. Classifier Guidance
# ============================================================
echo "=== Classifier Guidance ==="
python eval.py $COMMON \
--guidance-method classifier \
--constraints \
--n-sampling-steps $STEPS \
--guidance-lr 0.05 \
--gradient-start 0.0 \
--obstacle-weight 100.0 \
--dynamics-weight 10.0 \
--exp-name classifier
# ============================================================
# 4. SafeDiffuser RoS
# ============================================================
echo "=== SafeDiffuser RoS ==="
python eval.py $COMMON \
--guidance-method safediffuser \
--constraints \
--n-sampling-steps $STEPS_LONG \
--cbf-algorithm RoS \
--cbf-coefficient 1.0 \
--exp-name safediffuser_ros
# ============================================================
# 5. SafeDiffuser ReS
# ============================================================
echo "=== SafeDiffuser ReS ==="
python eval.py $COMMON \
--guidance-method safediffuser \
--constraints \
--n-sampling-steps $STEPS_LONG \
--cbf-algorithm ReS \
--cbf-coefficient 1.0 \
--cbf-slack-weight 1000.0 \
--cbf-relaxation-end 0.9 \
--exp-name safediffuser_res
# ============================================================
# 6. SafeDiffuser TVS
# ============================================================
echo "=== SafeDiffuser TVS ==="
python eval.py $COMMON \
--guidance-method safediffuser \
--constraints \
--n-sampling-steps $STEPS_LONG \
--cbf-algorithm TVS \
--cbf-coefficient 1.0 \
--cbf-sigmoid-bias 0.9 \
--cbf-sigmoid-input 256.0 \
--cbf-sigmoid-output 1.0 \
--exp-name safediffuser_tvs
# ============================================================
# 7. Primal-Dual
# ============================================================
echo "=== Primal-Dual ==="
python eval.py $COMMON \
--guidance-method primal-dual \
--constraints \
--n-sampling-steps $STEPS_LONG \
--pd-learning-rate 0.456 \ # Rescaled vals
--no-pd-use-cbfs \
--exp-name primal_dual
# ============================================================
# 8. Augmented Lagrangian
# ============================================================
echo "=== Augmented Lagrangian ==="
python eval.py $COMMON \
--guidance-method augmented-lagrangian \
--constraints \
--n-sampling-steps $STEPS_LONG \
--pd-learning-rate 0.456 \ # Rescaled vals
--pd-initial-penalty 1e-6 \
--pd-final-penalty 1e-5 \
--no-pd-use-cbfs \
--exp-name augmented_lagrangian
# ============================================================
# 9. Projection (IPOPT in noisy space)
# ============================================================
echo "=== Projection ==="
python eval.py $COMMON \
--guidance-method projection \
--constraints \
--dynamics-relaxation $RELAXATION \
--projection-start 0.5 \
--exp-name projection
# ============================================================
# 10. DiRecT (IPOPT in clean space)
# ============================================================
echo "=== DiRecT ==="
python eval.py $COMMON \
--guidance-method direct \
--constraints \
--dynamics-relaxation $RELAXATION \
--pred-control-weight 1.0 \ # Cost seeking behavior lambda=0.028
--pred-objective "" \
--pred-objective-scale 10.0 \
--guidance-last-steps 16 \
--exp-name direct
# echo "=== DiRecT + distance objective ==="
# python eval.py $COMMON \
# --guidance-method direct \
# --constraints \
# --dynamics-relaxation $RELAXATION \
# --pred-control-weight 1.0 \ # Cost seeking behavior lambda=0.028
# --pred-objective distance \
# --pred-objective-scale 10.0 \
# --guidance-last-steps 16 \
# --exp-name direct_distance
echo "============================================"
echo "All evaluations complete - $(date)"
echo "============================================"