no code implementations • 2 Dec 2022 • Eli Bronstein, Sirish Srinivasan, Supratik Paul, Aman Sinha, Matthew O'Kelly, Payam Nikdel, Shimon Whiteson
However, this approach produces agents that do not perform robustly in safety-critical settings, an issue that cannot be addressed by simply adding more data to the training set - we show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset.
no code implementations • 18 Oct 2022 • Eli Bronstein, Mark Palatucci, Dominik Notz, Brandyn White, Alex Kuefler, Yiren Lu, Supratik Paul, Payam Nikdel, Paul Mougin, Hongge Chen, Justin Fu, Austin Abrams, Punit Shah, Evan Racah, Benjamin Frenkel, Shimon Whiteson, Dragomir Anguelov
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving.
1 code implementation • 15 Sep 2022 • Mohammad Mahdavian, Payam Nikdel, Mahdi TaherAhmadi, Mo Chen
The proposed architecture divides human motion prediction into two parts: 1) the human trajectory, which is the hip joint 3D position over time and 2) the human pose which is the all other joints 3D positions over time with respect to a fixed hip joint.
no code implementations • 13 Sep 2022 • Payam Nikdel, Mohammad Mahdavian, Mo Chen
We show that our system outperforms the state-of-the-art in human motion prediction while it can predict diverse multi-motion future trajectories with hip movements
no code implementations • 5 Nov 2020 • Payam Nikdel, Richard Vaughan, Mo Chen
Our deep RL module implicitly estimates human trajectory and produces short-term navigational goals to guide the robot.
1 code implementation • 28 Sep 2019 • Changan Chen, Sha Hu, Payam Nikdel, Greg Mori, Manolis Savva
We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future.