Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness

• URL: http://arxiv.org/abs/2403.10454v2
• Date: Sun, 06 Oct 2024 17:34:15 GMT
• Title: Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness
• Authors: Aidan Curtis, George Matheos, Nishad Gothoskar, Vikash Mansinghka, Joshua Tenenbaum, Tomás Lozano-Pérez, Leslie Pack Kaelbling,
• Abstract summary: Integrated task and motion planning (TAMP) has proven to be a valuable approach to generalizable long-horizon robotic manipulation and navigation problems. These assumptions limit the ability of the planner to gather information and make decisions that are risk-aware. We propose a strategy for TAMP with Uncertainty and Risk Awareness (TAMPURA) that is capable of efficiently solving long-horizon planning problems with initial-state and action outcome uncertainty.
• Score: 34.77182116081535
• License:
• Abstract: Integrated task and motion planning (TAMP) has proven to be a valuable approach to generalizable long-horizon robotic manipulation and navigation problems. However, the typical TAMP problem formulation assumes full observability and deterministic action effects. These assumptions limit the ability of the planner to gather information and make decisions that are risk-aware. We propose a strategy for TAMP with Uncertainty and Risk Awareness (TAMPURA) that is capable of efficiently solving long-horizon planning problems with initial-state and action outcome uncertainty, including problems that require information gathering and avoiding undesirable and irreversible outcomes. Our planner reasons under uncertainty at both the abstract task level and continuous controller level. Given a set of closed-loop goal-conditioned controllers operating in the primitive action space and a description of their preconditions and potential capabilities, we learn a high-level abstraction that can be solved efficiently and then refined to continuous actions for execution. We demonstrate our approach on several robotics problems where uncertainty is a crucial factor and show that reasoning under uncertainty in these problems outperforms previously proposed determinized planning, direct search, and reinforcement learning strategies. Lastly, we demonstrate our planner on two real-world robotics problems using recent advancements in probabilistic perception.

Related papers

• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• A Model for Optimal Resilient Planning Subject to Fallible Actuators [28.11583381961291]
  We formulate the problem of planning with actuators susceptible to failure within the Markov Decision Processes (MDP) framework. The model captures utilization-driven malfunction and state-action dependent likelihoods of actuator failure. We identify opportunities to save computation through re-use, exploiting the observation that configurations yield closely related problems.
  arXiv  Detail & Related papers  (2024-05-18T22:07:38Z)
• Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity [0.659529078336196]
  Large language models (LLMs) exhibit advanced reasoning skills, enabling robots to comprehend natural language instructions. LLMs hallucination may result in robots executing plans that are misaligned with user goals or, in extreme cases, unsafe. This paper explores the concept of introspective planning as a systematic method for guiding LLMs in forming uncertainty-aware plans for robotic task execution.
  arXiv  Detail & Related papers  (2024-02-09T16:40:59Z)
• Unified Task and Motion Planning using Object-centric Abstractions of Motion Constraints [56.283944756315066]
  We propose an alternative TAMP approach that unifies task and motion planning into a single search. Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI search to yield physically feasible plans.
  arXiv  Detail & Related papers  (2023-12-29T14:00:20Z)
• Risk-reducing design and operations toolkit: 90 strategies for managing risk and uncertainty in decision problems [65.268245109828]
  This paper develops a catalog of such strategies and develops a framework for them. It argues that they provide an efficient response to decision problems that are seemingly intractable due to high uncertainty. It then proposes a framework to incorporate them into decision theory using multi-objective optimization.
  arXiv  Detail & Related papers  (2023-09-06T16:14:32Z)
• Achieving mouse-level strategic evasion performance using real-time computational planning [59.60094442546867]
  Planning is an extraordinary ability in which the brain imagines and then enacts evaluated possible futures. We develop a more efficient biologically-inspired planning algorithm, TLPPO, based on work on how the ecology of an animal governs the value of spatial planning. We compare the performance of a real-time agent using TLPPO against the performance of live mice, all tasked with evading a robot predator.
  arXiv  Detail & Related papers  (2022-11-04T18:34:36Z)
• Anytime Stochastic Task and Motion Policies [12.72186877599064]
  We present a new approach for integrated task and motion planning in settings. Our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion.
  arXiv  Detail & Related papers  (2021-08-28T00:23:39Z)
• Risk Conditioned Neural Motion Planning [14.018786843419862]
  Risk-bounded motion planning is an important yet difficult problem for safety-critical tasks. We propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic. We show the advantage of our model in terms of both computational time and plan quality, compared to a state-of-the-art mathematical programming baseline.
  arXiv  Detail & Related papers  (2021-08-04T05:33:52Z)
• Active Learning of Abstract Plan Feasibility [17.689758291966502]
  We present an active learning approach to efficiently acquire an APF predictor through task-independent, curious exploration on a robot. We leverage an infeasible subsequence property to prune candidate plans in the active learning strategy, allowing our system to learn from less data. In a stacking domain where objects have non-uniform mass distributions, we show that our system permits real robot learning of an APF model in four hundred self-supervised interactions.
  arXiv  Detail & Related papers  (2021-07-01T18:17:01Z)
• iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on Robots [46.13039152809055]
  We present a novel algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines.
  arXiv  Detail & Related papers  (2020-04-18T17:46:59Z)

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.