no code implementations • 8 Feb 2024 • Boyi Li, Yue Wang, Jiageng Mao, Boris Ivanovic, Sushant Veer, Karen Leung, Marco Pavone
Adapting driving behavior to new environments, customs, and laws is a long-standing problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs).
no code implementations • NeurIPS 2023 • Apoorva Sharma, Sushant Veer, Asher Hancock, Heng Yang, Marco Pavone, Anirudha Majumdar
To remedy this, recent work has proposed learning model and score function parameters using data to directly optimize the efficiency of the ICP prediction sets.
no code implementations • 27 Oct 2023 • Yuxiao Chen, Sushant Veer, Peter Karkus, Marco Pavone
In particular, IJP jointly optimizes over the behavior of the ego and the surrounding agents and leverages deep-learned prediction models as prediction priors that the join trajectory optimization tries to stay close to.
no code implementations • 3 Jul 2023 • Sushant Veer, Apoorva Sharma, Marco Pavone
Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios.
1 code implementation • 31 Oct 2022 • Ziyuan Zhong, Davis Rempe, Danfei Xu, Yuxiao Chen, Sushant Veer, Tong Che, Baishakhi Ray, Marco Pavone
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles.
no code implementations • 16 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.
no code implementations • 16 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.
no code implementations • 28 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.
1 code implementation • 25 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 drawn from a different distribution than the ones used to train the robot.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 24 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.
2 code implementations • 5 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.
no code implementations • 16 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.
1 code implementation • 28 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).