Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Kousik, Shreyas"'
Autor:
Jung, Wonsuhk, Anthony, Dennis, Mishra, Utkarsh A., Arachchige, Nadun Ranawaka, Bronars, Matthew, Xu, Danfei, Kousik, Shreyas
Imitation learning (IL) has shown great success in learning complex robot manipulation tasks. However, there remains a need for practical safety methods to justify widespread deployment. In particular, it is important to certify that a system obeys h
Externí odkaz:
http://arxiv.org/abs/2409.19190
Autor:
Chung, Long Kiu, Jung, Wonsuhk, Pullabhotla, Srivatsank, Shinde, Parth, Sunil, Yadu, Kota, Saihari, Batista, Luis Felipe Wolf, Pradalier, Cédric, Kousik, Shreyas
In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's performance when the obstacles form a narrow gap and the robot is a blac
Externí odkaz:
http://arxiv.org/abs/2409.13195
Autor:
Kong, Chuizheng, Qiu, Alex, Wibowo, Idris, Ren, Marvin, Dhori, Aishik, Ling, Kai-Shu, Hu, Ai-Ping, Kousik, Shreyas
Effective pollination is a key challenge for indoor farming, since bees struggle to navigate without the sun. While a variety of robotic system solutions have been proposed, it remains difficult to autonomously check that a flower has been sufficient
Externí odkaz:
http://arxiv.org/abs/2409.12311
Autor:
Shamsah, Abdulaziz, Agarwal, Krishanu, Katta, Nigam, Raju, Abirath, Kousik, Shreyas, Zhao, Ye
This study addresses the challenge of social bipedal navigation in a dynamic, human-crowded environment, a research area largely underexplored in legged robot navigation. We present a zonotope-based framework that couples prediction and motion planni
Externí odkaz:
http://arxiv.org/abs/2406.17151
The past few years have seen immense progress on two fronts that are critical to safe, widespread mobile robot deployment: predicting uncertain motion of multiple agents, and planning robot motion under uncertainty. However, the numerical methods req
Externí odkaz:
http://arxiv.org/abs/2406.01814
Real-time Model Predictive Control with Zonotope-Based Neural Networks for Bipedal Social Navigation
This study addresses the challenge of bipedal navigation in a dynamic human-crowded environment, a research area that remains largely underexplored in the field of legged navigation. We propose two cascaded zonotope-based neural networks: a Pedestria
Externí odkaz:
http://arxiv.org/abs/2403.16485
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments. In the literature, there has been an extensive focus on object labeling and exhaustive scene graph generation; less ef
Externí odkaz:
http://arxiv.org/abs/2403.07076
Autonomous mobile robots must maintain safety, but should not sacrifice performance, leading to the classical reach-avoid problem: find a trajectory that is guaranteed to reach a goal and avoid obstacles. This paper addresses the near danger case, al
Externí odkaz:
http://arxiv.org/abs/2402.15604
Connected autonomous vehicles (CAVs) promise to enhance safety, efficiency, and sustainability in urban transportation. However, this is contingent upon a CAV correctly predicting the motion of surrounding agents and planning its own motion safely. D
Externí odkaz:
http://arxiv.org/abs/2309.07504
Autor:
Michaux, Jonathan, Holmes, Patrick, Zhang, Bohao, Chen, Che, Wang, Baiyue, Sahgal, Shrey, Zhang, Tiancheng, Dey, Sidhartha, Kousik, Shreyas, Vasudevan, Ram
Ensuring safe, real-time motion planning in arbitrary environments requires a robotic manipulator to avoid collisions, obey joint limits, and account for uncertainties in the mass and inertia of objects and the robot itself. This paper proposes Auton
Externí odkaz:
http://arxiv.org/abs/2301.13308