AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents

• URL: http://arxiv.org/abs/2403.12835v1
• Date: Tue, 19 Mar 2024 15:41:39 GMT
• Title: AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents
• Authors: Jieming Cui, Tengyu Liu, Nian Liu, Yaodong Yang, Yixin Zhu, Siyuan Huang,
• Abstract summary: We propose AnySkill, a novel hierarchical method that learns physically plausible interactions following open-vocabulary instructions. Our approach begins by developing a set of atomic actions via a low-level controller trained via imitation learning. An important feature of our method is the use of image-based rewards for the high-level policy, which allows the agent to learn interactions with objects without manual reward engineering.
• Score: 58.807802111818994
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Traditional approaches in physics-based motion generation, centered around imitation learning and reward shaping, often struggle to adapt to new scenarios. To tackle this limitation, we propose AnySkill, a novel hierarchical method that learns physically plausible interactions following open-vocabulary instructions. Our approach begins by developing a set of atomic actions via a low-level controller trained via imitation learning. Upon receiving an open-vocabulary textual instruction, AnySkill employs a high-level policy that selects and integrates these atomic actions to maximize the CLIP similarity between the agent's rendered images and the text. An important feature of our method is the use of image-based rewards for the high-level policy, which allows the agent to learn interactions with objects without manual reward engineering. We demonstrate AnySkill's capability to generate realistic and natural motion sequences in response to unseen instructions of varying lengths, marking it the first method capable of open-vocabulary physical skill learning for interactive humanoid agents.

Related papers

• STEER: Flexible Robotic Manipulation via Dense Language Grounding [16.97343810491996]
  STEER is a robot learning framework that bridges high-level, commonsense reasoning with precise, flexible low-level control. Our approach translates complex situational awareness into actionable low-level behavior through training language-grounded policies with dense annotation.
  arXiv  Detail & Related papers  (2024-11-05T18:48:12Z)
• Text-Aware Diffusion for Policy Learning [8.32790576855495]
  We propose Text-Aware Diffusion for Policy Learning (TADPoLe), which uses a pretrained, frozen text-conditioned diffusion model to compute dense zero-shot reward signals for text-aligned policy learning. We show that TADPoLe is able to learn policies for novel goal-achievement and continuous locomotion behaviors specified by natural language, in both Humanoid and Dog environments.
  arXiv  Detail & Related papers  (2024-07-02T03:08:20Z)
• Interpretable Robotic Manipulation from Language [11.207620790833271]
  We introduce an explainable behavior cloning agent, named Ex-PERACT, specifically designed for manipulation tasks. At the top level, the model is tasked with learning a discrete skill code, while at the bottom level, the policy network translates the problem into a voxelized grid and maps the discretized actions to voxel grids. We evaluate our method across eight challenging manipulation tasks utilizing the RLBench benchmark, demonstrating that Ex-PERACT not only achieves competitive policy performance but also effectively bridges the gap between human instructions and machine execution in complex environments.
  arXiv  Detail & Related papers  (2024-05-27T11:02:21Z)
• Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition [63.95111791861103]
  Existing methods typically adapt pretrained image-text models to the video domain. We argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. Our method not only sets new SOTA performance but also possesses excellent interpretability.
  arXiv  Detail & Related papers  (2023-12-04T02:31:38Z)
• Learning to Act from Actionless Videos through Dense Correspondences [87.1243107115642]
  We present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments. Our method leverages images as a task-agnostic representation, encoding both the state and action information, and text as a general representation for specifying robot goals. We demonstrate the efficacy of our approach in learning policies on table-top manipulation and navigation tasks.
  arXiv  Detail & Related papers  (2023-10-12T17:59:23Z)
• Physically Plausible Full-Body Hand-Object Interaction Synthesis [32.83908152822006]
  We propose a physics-based method for synthesizing dexterous hand-object interactions in a full-body setting. Existing methods often focus on isolated segments of the interaction process and rely on data-driven techniques that may result in artifacts.
  arXiv  Detail & Related papers  (2023-09-14T17:55:18Z)
• Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance [71.36749876465618]
  We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks. Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples. experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
  arXiv  Detail & Related papers  (2022-12-19T22:50:40Z)
• Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation [118.27432851053335]
  This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track. The No Interaction track targets for learning policies from pre-collected demonstration trajectories. In this track, we design a Heuristic Rule-based Method (HRM) to trigger high-quality object manipulation by decomposing the task into a series of sub-tasks. For each sub-task, the simple rule-based controlling strategies are adopted to predict actions that can be applied to robotic arms.
  arXiv  Detail & Related papers  (2022-06-13T16:20:42Z)
• ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters [123.88692739360457]
  General-purpose motor skills enable humans to perform complex tasks. These skills also provide powerful priors for guiding their behaviors when learning new tasks. We present a framework for learning versatile and reusable skill embeddings for physically simulated characters.
  arXiv  Detail & Related papers  (2022-05-04T06:13:28Z)
• Language-Conditioned Imitation Learning for Robot Manipulation Tasks [39.40937105264774]
  We introduce a method for incorporating unstructured natural language into imitation learning. At training time, the expert can provide demonstrations along with verbal descriptions in order to describe the underlying intent. The training process then interrelates these two modalities to encode the correlations between language, perception, and motion. The resulting language-conditioned visuomotor policies can be conditioned at runtime on new human commands and instructions.
  arXiv  Detail & Related papers  (2020-10-22T21:49:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.