NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors

• URL: http://arxiv.org/abs/2311.01530v4
• Date: Sat, 05 Oct 2024 21:45:43 GMT
• Title: NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors
• Authors: Shuo Cheng, Caelan Garrett, Ajay Mandlekar, Danfei Xu,
• Abstract summary: We propose to combine two paradigms: Neural Object Descriptors (NODs) that produce generalizable object-centric features and Task and Motion Planning (TAMP) frameworks that chain short-horizon skills to solve multi-step tasks. We introduce NOD-TAMP, a TAMP-based framework that extracts short manipulation trajectories from a handful of human demonstrations, adapts these trajectories using NOD features, and composes them to solve broad long-horizon, contact-rich tasks.
• Score: 16.475094344344512
• License:
• Abstract: Solving complex manipulation tasks in household and factory settings remains challenging due to long-horizon reasoning, fine-grained interactions, and broad object and scene diversity. Learning skills from demonstrations can be an effective strategy, but such methods often have limited generalizability beyond training data and struggle to solve long-horizon tasks. To overcome this, we propose to synergistically combine two paradigms: Neural Object Descriptors (NODs) that produce generalizable object-centric features and Task and Motion Planning (TAMP) frameworks that chain short-horizon skills to solve multi-step tasks. We introduce NOD-TAMP, a TAMP-based framework that extracts short manipulation trajectories from a handful of human demonstrations, adapts these trajectories using NOD features, and composes them to solve broad long-horizon, contact-rich tasks. NOD-TAMP solves existing manipulation benchmarks with a handful of demonstrations and significantly outperforms prior NOD-based approaches on new tabletop manipulation tasks that require diverse generalization. Finally, we deploy NOD-TAMP on a number of real-world tasks, including tool-use and high-precision insertion. For more details, please visit https://nodtamp.github.io/.

Related papers

• MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation [52.739500459903724]
  Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation. We propose a novel multi-agent LLM framework that distributes high-level planning and low-level control code generation across specialized LLM agents. We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-11-26T17:53:44Z)
• LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner [9.044939946653002]
  Language models (LMs) possess a strong capability to comprehend natural language, making them effective in translating human instructions into detailed plans for simple robot tasks. We propose a Language Model-Driven Multi-Agent PDDL Planner (LaMMA-P), a novel multi-agent task planning framework. LaMMA-P integrates the strengths of the LMs' reasoning capability and the traditional search planner to achieve a high success rate and efficiency.
  arXiv  Detail & Related papers  (2024-09-30T17:58:18Z)
• Learning Task Planning from Multi-Modal Demonstration for Multi-Stage Contact-Rich Manipulation [26.540648608911308]
  In this paper, we introduce an in-context learning framework that incorporates tactile and force-torque information from human demonstrations. We propose a bootstrapped reasoning pipeline that sequentially integrates each modality into a comprehensive task plan. This task plan is then used as a reference for planning in new task configurations.
  arXiv  Detail & Related papers  (2024-09-18T10:36:47Z)
• Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation [49.43094200366251]
  We propose a novel approach for few-shot adaptation to unseen tasks that exploits the semantic understanding of task decomposition. Our method, Policy Adaptation via Language Optimization (PALO), combines a handful of demonstrations of a task with proposed language decompositions. We find that PALO is able of consistently complete long-horizon, multi-tier tasks in the real world, outperforming state of the art pre-trained generalist policies.
  arXiv  Detail & Related papers  (2024-08-29T03:03:35Z)
• LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation [38.66406497318709]
  This work focuses on the tabletop manipulation task and releases a simulation benchmark, textitLoHoRavens, which covers various long-horizon reasoning aspects spanning color, size, space, arithmetics and reference. We investigate two methods of bridging the modality gap: caption generation and learnable interface for incorporating explicit and implicit observation feedback to the LLM.
  arXiv  Detail & Related papers  (2023-10-18T14:53:14Z)
• 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)
• Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks [65.23947618404046]
  We introduce a framework that acquires goal-conditioned policies for unseen temporally extended tasks via offline reinforcement learning on broad data. When faced with a novel task goal, the framework uses an affordance model to plan a sequence of lossy representations as subgoals that decomposes the original task into easier problems. We show that our framework can be pre-trained on large-scale datasets of robot experiences from prior work and efficiently fine-tuned for novel tasks, entirely from visual inputs without any manual reward engineering.
  arXiv  Detail & Related papers  (2022-10-12T21:46:38Z)
• Learning Neuro-Symbolic Skills for Bilevel Planning [63.388694268198655]
  Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches, such as task and motion planning (TAMP), address these challenges by decomposing decision-making into two or more levels of abstraction. Our main contribution is a method for learning parameterized polices in combination with operators and samplers.
  arXiv  Detail & Related papers  (2022-06-21T19:01:19Z)
• Hierarchical Few-Shot Imitation with Skill Transition Models [66.81252581083199]
  Few-shot Imitation with Skill Transition Models (FIST) is an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments.
  arXiv  Detail & Related papers  (2021-07-19T15:56:01Z)

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.