Search Results for author: Sushant Veer

Found 8 papers, 3 papers with code

Learning Provably Robust Motion Planners Using Funnel Libraries

no code implementations16 Nov 2021 Ali Ekin Gurgen, Anirudha Majumdar, Sushant Veer

This paper presents an approach for learning motion planners that are accompanied with probabilistic guarantees of success on new environments that hold uniformly for any disturbance to the robot's dynamics within an admissible set.

Generalization Bounds

Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data

no code implementations16 Nov 2021 Abhinav Agarwal, Sushant Veer, Allen Z. Ren, Anirudha Majumdar

The key idea behind our approach is to utilize the generative model in order to implicitly specify a prior over policies.

Interactive Dynamic Walking: Learning Gait Switching Policies with Generalization Guarantees

no code implementations28 Sep 2021 Prem Chand, Sushant Veer, Ioannis Poulakakis

In this paper, we consider the problem of adapting a dynamically walking bipedal robot to follow a leading co-worker while engaging in tasks that require physical interaction.

Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot Learning

1 code implementation25 Jun 2021 Alec Farid, Sushant Veer, Divyanshu Pachisia, Anirudha Majumdar

Our goal is to perform out-of-distribution (OOD) detection, i. e., to detect when a robot is operating in environments that are drawn from a different distribution than the environments used to train the robot.

Out-of-Distribution Detection

LagNetViP: A Lagrangian Neural Network for Video Prediction

no code implementations24 Oct 2020 Christine Allen-Blanchette, Sushant Veer, Anirudha Majumdar, Naomi Ehrich Leonard

In this paper, we introduce a video prediction model where the equations of motion are explicitly constructed from learned representations of the underlying physical quantities.

Acrobot Video Prediction

Generalization Guarantees for Imitation Learning

2 code implementations5 Aug 2020 Allen Z. Ren, Sushant Veer, Anirudha Majumdar

Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert's policies.

Generalization Bounds Imitation Learning

CoNES: Convex Natural Evolutionary Strategies

no code implementations16 Jul 2020 Sushant Veer, Anirudha Majumdar

We present a novel algorithm -- convex natural evolutionary strategies (CoNES) -- for optimizing high-dimensional blackbox functions by leveraging tools from convex optimization and information geometry.

Probably Approximately Correct Vision-Based Planning using Motion Primitives

1 code implementation28 Feb 2020 Sushant Veer, Anirudha Majumdar

This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i. e., environments unseen during training).

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