Integration of Robot and Scene Kinematics for Sequential Mobile Manipulation Planning
🤖 AI Summary
Coordinating whole-body motion during long-horizon, multi-step mobile manipulation involving articulated objects remains challenging due to the coupling between navigation and manipulation in structured environments.
Method: This paper introduces the Augmented Configuration Space (A-Space), which abstracts scene structure into a kinematic model unified with the robot’s own kinematics, enabling joint navigation–manipulation planning. The approach integrates symbolic task planning, optimization-based trajectory planning, and an intermediate-layer refinement mechanism within a three-tiered task–action–trajectory framework to ensure cross-scene generalization and long-term feasibility. A-Space jointly models joint reachability and supports unified constraint solving.
Results: In simulation, task success rate improves by 84.6%; on a real robot, the method successfully executes up to 14 consecutive manipulation steps across 17 diverse scenes involving seven categories of rigid and articulated objects.
Application Category
📝 Abstract
We present a Sequential Mobile Manipulation Planning (SMMP) framework that can solve long-horizon multi-step mobile manipulation tasks with coordinated whole-body motion, even when interacting with articulated objects. By abstracting environmental structures as kinematic models and integrating them with the robot's kinematics, we construct an Augmented Configuration Apace (A-Space) that unifies the previously separate task constraints for navigation and manipulation, while accounting for the joint reachability of the robot base, arm, and manipulated objects. This integration facilitates efficient planning within a tri-level framework: a task planner generates symbolic action sequences to model the evolution of A-Space, an optimization-based motion planner computes continuous trajectories within A-Space to achieve desired configurations for both the robot and scene elements, and an intermediate plan refinement stage selects action goals that ensure long-horizon feasibility. Our simulation studies first confirm that planning in A-Space achieves an 84.6% higher task success rate compared to baseline methods. Validation on real robotic systems demonstrates fluid mobile manipulation involving (i) seven types of rigid and articulated objects across 17 distinct contexts, and (ii) long-horizon tasks of up to 14 sequential steps. Our results highlight the significance of modeling scene kinematics into planning entities, rather than encoding task-specific constraints, offering a scalable and generalizable approach to complex robotic manipulation.
Problem
Research questions and friction points this paper is trying to address.
Planning long-horizon mobile manipulation tasks with articulated objects
Integrating robot and scene kinematics into unified configuration space
Ensuring coordinated whole-body motion and sequential action feasibility
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates robot and scene kinematics for unified planning
Constructs Augmented Configuration Space for task constraints
Uses tri-level framework for efficient motion planning
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