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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Committee Members:
Professor Henrik Christensen (Advisor), ECE & UCSD
Stephen Balakirsky, PhD - GTRI
Professor Patricio Vela - ECE
Professor Aaron Ames - CalTech
Professor Dmitry Berenson - University of Michigan
Abstract:
This document introduces a novel technique for addressing the fragility of solutions that is common to configuration space-based planners. Both the state and the pre- and post-conditions of actions are represented as volumes of the configuration space. Feasibility of the various action types is determined by a subset test: if the set of possible states is a subset of the pre-condition set of a given action, then that action is feasible. This system is then incorporated into a planning system which is demonstrated on a number of household, scientific, and industrial application domains.
In the development of this framework, we build off of the idea of affordances: a high-level technique for reasoning about the space of latent action possibilities between an agent and its environment. In the language of affordances, our contributions are as follows:
* We formalize the computational notion of an affordance by introducing a set-based formulation of action pre-conditions and post-conditions.
* We define the feasibility of an action as an inclusion predicate between convex sets representing the possible system configuration and the parameterized action pre-conditions.
* We introduce a novel planning algorithm incorporating the previous contributions to jointly reason over information gathering and state transformation.
* We demonstrate the proposed system on a variety of simulated and real scenarios derived from household, scientific research, and industrial application domains.
This approach has a number of indirect benefits as well. First, the definition of action predicate sets depends on deep knowledge of the agent in question; as a result, the affordance representation specializes to a variety of agents while requiring only commonly-used mechanical and kinematic parameters. Second, as actions may serve to expand or contract the belief state, information-gathering actions may be planned without the need for e.g. a bespoke entropy minimization framework.
These benefits do incur some trade-offs, however. An explicit enumeration of possible actions is required, as are detailed models of their input-output behavior. These models may be developed from existing theory, via simulation, or by physical experimentation. In principle, such models could be discovered by self-experimentation or learning from demonstration, but these techniques are outside the scope of this work.
