Robotic manipulation relies on analytical or learned models to simulate the system dynamics. These models are often inaccurate and based on offline information, so that the robot planner is unable to cope with mismatches between the expected and the actual behavior of the system (e.g., the presence of an unexpected obstacle). In these situations, the robot should use information gathered online to correct its planning strategy and adapt to the actual system response. We propose a sampling-based motion planning approach that uses an estimate of the model error and online observations to correct the planning strategy at each new replanning. Our approach adapts the cost function and the sampling bias of a kinodynamic motion planner when the outcome of the executed transitions is different from the expected one (e.g., when the robot unexpectedly collides with an obstacle) so that future trajectories will avoid unreliable motions. To infer the properties of a new transition, we introduce the notion of context-awareness, i.e., we store local environment information for each executed transition and avoid new transitions with context similar to previous unreliable ones. This is helpful for leveraging online information even if the simulated transitions are far (in the state-and-action space) from the executed ones. Simulation and experimental results show that the proposed approach increases the success rate in execution and reduces the number of replannings needed to reach the goal.

翻译：机器人操作依赖于解析或学习模型来模拟系统动力学。这些模型通常不准确且基于离线信息，导致机器人规划器无法应对系统预期行为与实际行为之间的不匹配（例如，出现意外障碍物）。针对此类情况，机器人应利用在线收集的信息修正规划策略，并自适应实际系统响应。本文提出一种基于采样的运动规划方法，利用模型误差估计和在线观测在每个重规划阶段修正规划策略。当已执行动作的结果与预期不一致时（例如机器人意外碰撞障碍物），该方法自适应调整运动动力学规划器的代价函数和采样偏差，使未来轨迹避免不可靠动作。为推断新动作的特性，我们引入上下文感知概念，即存储每个已执行动作的局部环境信息，并避免选择与先前不可靠动作具有相似上下文的动作。即使仿真动作与已执行动作在状态-动作空间上距离较远，该方法也能有效利用在线信息。仿真与实验结果表明，所提方法可提高执行成功率，并减少到达目标所需的重规划次数。