no code implementations • 11 Dec 2023 • Xiao Zhang, David Yunis, Michael Maire
We present an approach for analyzing grouping information contained within a neural network's activations, permitting extraction of spatial layout and semantic segmentation from the behavior of large pre-trained vision models.
no code implementations • 8 Sep 2023 • David Yunis, Justin Jung, Falcon Dai, Matthew Walter
Exploration in sparse-reward reinforcement learning is difficult due to the requirement of long, coordinated sequences of actions in order to achieve any reward.
no code implementations • 30 Jun 2023 • Takuma Yoneda, Jiading Fang, Peng Li, Huanyu Zhang, Tianchong Jiang, Shengjie Lin, Ben Picker, David Yunis, Hongyuan Mei, Matthew R. Walter
In this paper, we explore a new dimension in which large language models may benefit robotics planning.
3 code implementations • ICLR 2018 • Charles Schaff, David Yunis, Ayan Chakrabarti, Matthew R. Walter
The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment.
1 code implementation • 24 Mar 2017 • Charles Schaff, David Yunis, Ayan Chakrabarti, Matthew R. Walter
The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements.