Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday environments. These skills can be assessed based on a system's ability to coordinate complex holistic movements and multiple contact interactions when solving different tasks. However, existing approaches have been merely able to shape such behaviors with hand-crafted state machines, densely engineered rewards, or pre-recorded expert demonstrations. Here, we propose a minimally-guided framework that automatically discovers whole-body trajectories jointly with contact schedules for solving general loco-manipulation tasks in pre-modeled environments. The key insight is that multi-modal problems of this nature can be formulated and treated within the context of integrated Task and Motion Planning (TAMP). An effective bilevel search strategy is achieved by incorporating domain-specific rules and adequately combining the strengths of different planning techniques: trajectory optimization and informed graph search coupled with sampling-based planning. We showcase emergent behaviors for a quadrupedal mobile manipulator exploiting both prehensile and non-prehensile interactions to perform real-world tasks such as opening/closing heavy dishwashers and traversing spring-loaded doors. These behaviors are also deployed on the real system using a two-layer whole-body tracking controller.

翻译：腿臂协同操作的规划技能对于拓展机器人在日常环境中的实用性至关重要。这些技能可通过系统在解决不同任务时协调复杂整体运动与多接触交互的能力来评估。然而，现有方法仅能通过手工设计的状态机、密集设计的奖励函数或预录制的专家示教来塑造此类行为。本文提出了一种最小引导框架，该框架可自动发现全身运动轨迹及接触时序，以解决预建模环境中的通用腿臂协同操作任务。核心洞察在于：此类多模态问题可被纳入集成式任务与运动规划（TAMP）框架进行建模与处理。通过融入领域特定规则并合理结合不同规划技术的优势（轨迹优化、信息引导图搜索与基于采样的规划），我们实现了高效的双层搜索策略。我们展示了四足移动操作机器人利用抓取与非抓取交互执行真实世界任务（如开关重型洗碗机、穿越弹簧门）时涌现的自主行为。这些行为亦通过双层全身跟踪控制器部署于真实系统。