Planning Robust Strategies for Constructing Multi-object Arrangements
Author(s)Anders, Ariel; Kaelbling, Leslie; Lozano-Perez, Tomas
Learning and Intelligent Systems
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A crucial challenge in robotics is achieving reliable results in spite of sensing and control uncertainty. A prominent strategy for dealing with uncertainty is to construct a feedback policy, where actions are chosen as a function of the current state estimate. However, constructing such policies is computationally very difficult. An alternative strategy is conformant planning which finds open-loop action sequences that achieve the goal for all input states and action outcomes. In this work, we investigate the conformant planning approach to robot manipulation. In particular, we tackle the problem of pushing multiple objects simultaneously to achieve a specified arrangement. Conformant planning is a belief-state planning problem. A belief state is the set of all possible states of the world, and the goal is to find a sequence of actions that will bring an initial belief state to a goal belief state To do forward belief-state planning, we created a deterministic belief-state transition model from supervised learning based on physics simulations. A key pitfall in conformant planning is that the complexity of the belief state tends to increase with each operation, making it increasingly harder to compute the effect of actions. This work explores the idea that we can construct conformant plans for robot manipulation by only using actions resulting in compact belief states.
manipulation, robotics, machine learning, belief space, planning, uncertainty