jamesheald/joint-space-empowerment

Joint-Space Empowerment for Dexterous Coordination in Tendon-Driven Hands

This repository contains the official trained policies for

Joint-Space Empowerment for Dexterous Coordination in Tendon-Driven Hands
James Heald, Vittorio Caggiano, Vikash Kumar, Maneesh Sahani
ICML 2026 (Spotlight)
Paper | Code | Project Page

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📂 Repository Structure

The files are organized by task environment and training seed:

joint-space-empowerment
├── Adroit
│   ├── BaodingBalls
│   │   ├── CustomAdroitBaodingP1-v1.yaml                      # Environment config
│   │   ├── seed_0
│   │   │   ├── rl_model_10000000_steps.zip                    # Trained model
│   │   │   └── rl_model_vecnormalize_10000000_steps.pkl       # Observation normalization statistics
│   │   ├── seed_1  …  seed_4
│   ├── DieReorient
│   │   └── ...
│   ├── KeyTurn
│   │   └── ...
│   └── PenTwirl
│       └── ...
├── MyoHand
│   ├── BaodingBalls
│   │   └── ...
│   ├── DieReorient
│   │   └── ...
│   ├── KeyTurn
│   │   └── ...
│   ├── PenTwirl
│   │   └── ...
│   ├── Reorient100
│   │   ├── Pretraining_Play
│   │   │   ├── seed_0
│   │   │   │   ├── rl_model_replay_buffer_1000000_steps.pkl   # Replay buffer
│   │   │   │   └── rl_model_vecnormalize_1000000_steps.pkl
│   │   │   ├── seed_1  …  seed_4
│   │   └── Training
│   │       └── ...
│   └── Reorient8-sparse
│       └── ...
└── README.md

Loading Example

Prerequisites: clone and install the JoSE repository.

The play.py script in the JoSE repository downloads a pretrained model and executes it in the corresponding environment, allowing agent behaviour to be visualized. The script is called from the repo root as follows:

uv run python play.py --hand <hand> --task <task> --seed <seed>

Argument	Choices
--hand	Adroit, MyoHand
--task	BaodingBalls, DieReorient, KeyTurn, PenTwirl, Reorient8-sparse, Reorient100/Training
--seed	0, 1, 2, 3, 4, 5

Seeds vary by hand and task. See the HuggingFace model directory to see which seeds are available for each task-hand combination.