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carlq
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ecdiffuser-data

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Robotics
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Tags:
multi-object-manipulation
diffusion-models
behavioral-cloning
object-centric
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The dataset viewer is not available for this dataset.
The JWT signature verification failed. Check the signing key and the algorithm.
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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EC-Diffuser Dataset

This repository contains the datasets, pretrained agents, and Deep Latent Predictor (DLP) representations for the paper EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation.

EC-Diffuser proposes a novel behavioral cloning (BC) approach that leverages object-centric representations and an entity-centric Transformer with diffusion-based optimization. This enables efficient learning from offline image data for multi-object manipulation tasks, leading to substantial performance improvements and compositional generalization to novel object configurations and goals.

Sample Usage

The datasets, pretrained agents, and DLP representations provided here are intended for use with the official EC-Diffuser code repository. Below are instructions for setting up the environment, downloading the data, and using the provided scripts for evaluation and training.

Installation

Follow these steps to set up the environment (tested on Python 3.8):

  1. Create and activate a Conda environment:

    conda create -n dlp python=3.8
conda activate dlp

  2. Install main dependencies: The full list of dependencies can be found in the requirements.txt file within the code repository.

  3. Install Diffuser-related packages:

    cd diffuser
pip install -e .
cd ../

  4. Setup for the FrankaKitchen environment:

    Install D4RL by cloning the repository:

    git clone https://github.com/Farama-Foundation/d4rl.git
cd d4rl
pip install -e .
cd ../

  5. Finalize environment setup:

    Run the provided setup script:

    bash setup_env.sh

    (If the script requires sourcing, you can also run: source setup_env.sh)

Downloading Datasets

Download the required datasets, pretrained agents, and DLP representations from this Hugging Face dataset repository:

git lfs install
git clone https://huggingface.co/datasets/carlq/ecdiffuser-data

Evaluating a Pretrained Agent

You can evaluate the pretrained agents with the following commands. Replace CUDA_VISIBLE_DEVICES=0,1 with the GPU devices you wish to use (Note IsaacGym env has to be on GPU 0).

  • PushCube Agent:

    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/eval_agent.py --config config.plan_pandapush_pint --num_entity 3 --planning_only

  • PushT Agent:

    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/eval_agent.py --config config.plan_pandapush_pint --push_t --num_entity 3 --push_t_num_color 1 --planning_only

  • FrankaKitchen Agent:

    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/eval_agent.py --config config.plan_pandapush_pint --kitchen --planning_only

Training an Agent

Train your own agents using the commands below. Replace CUDA_VISIBLE_DEVICES=0,1 with the GPU devices you wish to use (Note IsaacGym env has to be on GPU 0).

  • Train a PushCube Agent (3 cubes):

    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/train.py --config config.pandapush_pint --num_entity 3

  • Train a PushT Agent (1 T-shaped object):

    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/train.py --config config.pandapush_pint --push_t --num_entity 1

  • Train a FrankaKitchen Agent:

    CUDA_VISIBLE_DEVICES=0,1 python diffuser/scripts/train_kitchen.py --config config.pandapush_pint --kitchen
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Paper for carlq/ecdiffuser-data