Randomized multi-modal motion planning for a humanoid robot manipulation task

Abstract

Robots that perform complex manipulation tasks must be able to generate strategies that make and break contact with the object. This requires reasoning in a motion space with a particular multi-modal structure, in which the state contains both a discrete mode (the contact state) and a continuous configuration (the robot and object poses). In this paper we address multi-modal motion planning in the common setting where the state is high-dimensional, and there are a continuous infinity of modes. We present a highly general algorithm, Random-MMP, that repeatedly attempts mode switches sampled at random. A major theoretical result is that Random-MMP is formally reliable and scalable, and its running time depends on certain properties of the multi-modal structure of the problem that are not explicitly dependent on dimensionality. We apply the planner to a manipulation task on the Honda humanoid robot, where the robot is asked to push an object to a desired location on a cluttered table, and the robot is restricted to switch between walking, reaching, and pushing modes. Experiments in simulation and on the real robot demonstrate that Random-MMP solves problem instances that require several carefully chosen pushes in minutes on a PC.

A hierarchical approach to manipulation with diverse actions

“…He showed how to create a two-level roadmap of modes and configurations using interspersed mode and configuration sampling. Later Hauser and Ng-Throw-Hing [16] extended this work to domains like manipulation where the mode space is continuous and were able to find paths for a walking robot pushing an object on a table. However, that work required the implementation of complicated mode samplers and a number of heuristics, some of which took substantial pre-processing time.…”

“…For adaptation to continuousmode problems, such as the DAMA problem, we follow Hauser [16] and describe the set of continuous modes as a finite, discrete set of mode families. Mode families partition a continuous mode set using a co-parameter that varies to describe each of the different modes.…”

“…In this section, we first review the explicit multi-modal planning work [1], [16], showing that it cannot be used for some manipulation problems, and then discuss using the multi-modal structure to create hierarchies.…”

Manipulation with Multiple Action Types

“…He showed how to create a two-level roadmap of modes and configurations using interspersed mode and configuration sampling. Later Hauser [8] extended this work to domains like manipulation where the mode space is continuous and used interleaved intra-mode and inter-mode planning to find paths for a walking robot pushing an object on a table. However, that work required the implementation of complicated mode samplers and a number of heuristics, some of which took substantial pre-processing time.…”

Probabilistic Completeness of Randomized Possibility Graphs Applied to Bipedal Walking in Semi-unstructured Environments

“…1. The Multi-modal Probabilistic Roadmap (MMPRM) approach by Hauser et al [9,10,11] uses randomized sampling to find whole body motions over a set of predetermined environmental contacts. Given a suitable set of environmental contact points, this method is proven to be probabilistically complete for the full range of quasi-static motion of the robot.…”