Contact as a Decision Variable: Capability-Tradeoff Contact Selection for Legged Loco-Manipulation

2026-09-24

AI Summary

This study addresses the computational bottleneck in legged mobile manipulation arising from the coupling between contact point selection and whole-body configuration by proposing the CTCS framework. This method models environmental support contacts as decision variables and innovatively integrates local sensitivity analysis with selective exact evaluation. Through candidate clustering, screening, and grouping, it efficiently predicts residual torques, end-effector reachability, and base mobility, balancing computational overhead while circumventing redundant whole-body optimization. Simulation and hardware experiments on the Unitree Go2 demonstrate that the proposed approach achieves approximately a threefold speedup over exhaustive evaluation while yielding near-optimal objective values, significantly outperforming fixed-contact or ground-only support baselines.

Abstract

In this paper, we study the joint selection of an environmental support contact and a whole-body configuration for a prescribed loco-manipulation task. A contact may provide greater physical support while restricting the motion required for the task. We formulate this problem through three capability measures: residual wrench, end-effector reach, and base mobility available after satisfying the task requirements, and we balance them against contact acquisition cost. Evaluating these capabilities for every candidate requires repeated whole-body optimizations. To reduce this computational cost, we propose Capability-Tradeoff Contact Selection (CTCS). CTCS screens candidates for contact and task feasibility, groups similar candidates within each surface, and predicts their capabilities from exact anchor evaluations using local sensitivity analysis. It checks these predictions through selective exact evaluations, ranks candidates by capability, and evaluates a shortlist exactly for final selection. We evaluate CTCS in simulations and hardware experiments using a Unitree Go2 quadruped with an AgileX NERO arm across $392$ task conditions with nine available support surfaces. Results show that CTCS outperforms ground-only and fixed-contact support, as it can select support surfaces that provide favorable capability trade-offs for the task. Compared with evaluating every candidate exactly, CTCS achieves approximately $3\times$ speedup while closely matching the resulting mean objective value.

Problem

• legged loco-manipulation
• contact selection
• whole-body configuration
• capability tradeoff

Innovation

• Contact Selection
• Loco-Manipulation
• Capability-Tradeoff
• Local Sensitivity Analysis
• Whole-Body Optimization