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Scott Chow

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3 papers

2 author rows

ICRA Conference 2021 Conference Paper

Compensating for Unmodeled Forces using Neural Networks in Soft Manipulator Planning

Scott Chow
Gina Olson
Geoffrey A. Hollinger

Soft manipulators made of deformable materials have great promise in applications that require additional flexibility and compliance; however, these characteristics also make them difficult to simulate accurately and quickly. The lack of a fast and accurate simulator prevents motion planners from generating feasible plans, which would enable soft robots to achieve more complex tasks, such as manipulation. In this work, we propose combining a simplified quasistatic model with a neural network that learns to compensate for unmodeled forces, such as friction and loads, in order to create a fast forward model for soft manipulators with multiple segments. We show that the resulting neural network model reduces average end effector position error by 62% compared to the quasistatic model, while still being fast enough for motion planning. We also incorporate this model into an RRT*-based planner and demonstrate that the plans generated using our model are more likely to be feasible when executed on hardware than plans generated with a simulator using the quasistatic model.

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AAMAS Conference 2018 Conference Paper

When Less is More: Reducing Agent Noise with Probabilistically Learning Agents

Jen Jen Chung
Scott Chow
Kagan Tumer

Distributed agents concurrently learning to coordinate in a multiagent system can suffer from considerable amounts of agent noise. This is the noise that arises from the non-stationarity of the learning environment for each individual agent since other agents in the system are also constantly updating their policies, thereby continually shifting the goal posts for successful coordination. In this work, we propose a method to reduce agent noise by allowing individual agents to probabilistically determine whether or not to undergo policy updates. We show that using this method to adapt the number of actively learning agents over time provides improvements in convergence speed of the team as a whole without affecting the final converged learning performance.

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AAAI Conference 2017 Conference Paper

Robust Execution of Probabilistic Temporal Plans

Kyle Lund
Sam Dietrich
Scott Chow
James Boerkoel

A critical challenge in temporal planning is robustly dealing with non-determinism, e. g. , the durational uncertainty of a robot’s activity due to slippage or other unexpected inﬂuences. Recent advances show that robustness is a better measure of solution quality than traditional metrics such as ﬂexibility. This paper introduces the Robust Execution Problem for ﬁnding maximally robust dispatch strategies for general probabilistic temporal planning problems. While generally intractable, we introduce approximate solution techniques— one that can be computed statically prior to the start of execution with robustness guarantees and one that dynamically adjusts to opportunities and setbacks during execution. We show empirically that our dynamic approach outperforms all known approaches in terms of execution success rate.

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