Object-Centric Task and Motion Planning in Dynamic Environments

Abstract

We address the problem of applying Task and Motion Planning (TAMP) in real world environments. TAMP combines symbolic and geometric reasoning to produce sequential manipulation plans, typically specified as joint-space trajectories, which are valid only as long as the environment is static and perception and control are highly accurate. In case of any changes in the environment, slow re-planning is required. We propose a TAMP algorithm that optimizes over Cartesian frames defined relative to target objects. The resulting plan then remains valid even if the objects are moving and can be executed by reactive controllers that adapt to these changes in real time. We apply our TAMP framework to a torque-controlled robot in a pick and place setting and demonstrate its ability to adapt to changing environments, inaccurate perception, and imprecise control, both in simulation and the real world.

References in corpus (6)

• The Fast Downward Planning System
• Sampling-Based Methods for Factored Task and Motion Planning
• Probabilistic Object Tracking using a Range Camera
• Depth-Based Object Tracking Using a Robust Gaussian Filter
• Towards Combining HTN Planning and Geometric Task Planning
• Combined Task and Motion Planning as Classical AI Planning

Cited by in corpus (8)

• Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly
• Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation
• A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches
• Receding Horizon Task and Motion Planning in Changing Environments
• Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning
• Multi-Modal MPPI and Active Inference for Reactive Task and Motion Planning
• A Conflict-driven Interface between Symbolic Planning and Nonlinear Constraint Solving
• Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning