HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation

• URL: http://arxiv.org/abs/2406.14655v1
• Date: Thu, 20 Jun 2024 18:21:24 GMT
• Title: HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation
• Authors: Jin Wang, Rui Dai, Weijie Wang, Luca Rossini, Francesco Ruscelli, Nikos Tsagarakis,
• Abstract summary: HYPERmotion is a framework that learns, selects and plans behaviors based on tasks in different scenarios. We combine reinforcement learning with whole-body optimization to generate motion for 38 actuated joints. Experiments in simulation and real-world show that learned motions can efficiently adapt to new tasks.
• Score: 7.01404330241523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enabling robots to autonomously perform hybrid motions in diverse environments can be beneficial for long-horizon tasks such as material handling, household chores, and work assistance. This requires extensive exploitation of intrinsic motion capabilities, extraction of affordances from rich environmental information, and planning of physical interaction behaviors. Despite recent progress has demonstrated impressive humanoid whole-body control abilities, they struggle to achieve versatility and adaptability for new tasks. In this work, we propose HYPERmotion, a framework that learns, selects and plans behaviors based on tasks in different scenarios. We combine reinforcement learning with whole-body optimization to generate motion for 38 actuated joints and create a motion library to store the learned skills. We apply the planning and reasoning features of the large language models (LLMs) to complex loco-manipulation tasks, constructing a hierarchical task graph that comprises a series of primitive behaviors to bridge lower-level execution with higher-level planning. By leveraging the interaction of distilled spatial geometry and 2D observation with a visual language model (VLM) to ground knowledge into a robotic morphology selector to choose appropriate actions in single- or dual-arm, legged or wheeled locomotion. Experiments in simulation and real-world show that learned motions can efficiently adapt to new tasks, demonstrating high autonomy from free-text commands in unstructured scenes. Videos and website: hy-motion.github.io/

Related papers

• Grounding Language Models in Autonomous Loco-manipulation Tasks [3.8363685417355557]
  We propose a novel framework that learns, selects, and plans behaviors based on tasks in different scenarios. We leverage the planning and reasoning features of the large language model (LLM), constructing a hierarchical task graph. Experiments in simulation and real-world using the CENTAURO robot show that the language model based planner can efficiently adapt to new loco-manipulation tasks.
  arXiv  Detail & Related papers  (2024-09-02T15:27:48Z)
• DISCO: Embodied Navigation and Interaction via Differentiable Scene Semantics and Dual-level Control [53.80518003412016]
  Building a general-purpose intelligent home-assistant agent skilled in diverse tasks by human commands is a long-term blueprint of embodied AI research. We study primitive mobile manipulations for embodied agents, i.e. how to navigate and interact based on an instructed verb-noun pair. We propose DISCO, which features non-trivial advancements in contextualized scene modeling and efficient controls.
  arXiv  Detail & Related papers  (2024-07-20T05:39:28Z)
• Human-Object Interaction from Human-Level Instructions [16.70362477046958]
  We present the first complete system that can synthesize object motion, full-body motion, and finger motion simultaneously from human-level instructions. Our experiments demonstrate the effectiveness of our high-level planner in generating plausible target layouts and our low-level motion generator in synthesizing realistic interactions for diverse objects.
  arXiv  Detail & Related papers  (2024-06-25T17:46:28Z)
• Generalizable Long-Horizon Manipulations with Large Language Models [91.740084601715]
  This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations. We create a challenging robotic manipulation task suite based on Pybullet for long-horizon task evaluation.
  arXiv  Detail & Related papers  (2023-10-03T17:59:46Z)
• VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models [38.503337052122234]
  Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation. We aim to synthesize robot trajectories for a variety of manipulation tasks given an open-set of instructions and an open-set of objects. We demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions.
  arXiv  Detail & Related papers  (2023-07-12T07:40:48Z)
• 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)
• iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes [54.04456391489063]
  iGibson is a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. Our environment contains fifteen fully interactive home-sized scenes populated with rigid and articulated objects. iGibson features enable the generalization of navigation agents, and that the human-iGibson interface and integrated motion planners facilitate efficient imitation learning of simple human demonstrated behaviors.
  arXiv  Detail & Related papers  (2020-12-05T02:14:17Z)
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
  ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
  arXiv  Detail & Related papers  (2020-08-18T08:05:15Z)
• Learning compositional models of robot skills for task and motion planning [39.36562555272779]
  We learn to use sensorimotor primitives to solve complex long-horizon manipulation problems. We use state-of-the-art methods for active learning and sampling. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions.
  arXiv  Detail & Related papers  (2020-06-08T20:45:34Z)
• SAPIEN: A SimulAted Part-based Interactive ENvironment [77.4739790629284]
  SAPIEN is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects. We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks.
  arXiv  Detail & Related papers  (2020-03-19T00:11:34Z)

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.