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Yash Oza

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

ICRA Conference 2024 Conference Paper

Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulator

Ramkumar Natarajan
Hanlan Yang
Qintong Xie
Yash Oza
Manash Pratim Das
Fahad Islam 0002
Muhammad Suhail Saleem
Howie Choset

We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile’s future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator’s dynamic limits and extreme time constraints (≤ 350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot’s joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions.

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IROS Conference 2021 Conference Paper

Disruption-Limited Planning for Robot Navigation in Dynamic Environments

Sandip Aine
Yash Oza
Maxim Likhachev

Path planning in the presence of dynamic obstacles is a fundamental problem in robotics with widespread applications. A typical approach to such problems is that a robot predicts the trajectories of dynamic obstacles, and plans its path while avoiding them. Such a formulation becomes limiting though for scenarios where an agent cannot complete its task efficiently, without disrupting the movement of dynamic obstacles. For example, when merging in heavy traffic or navigating through crowded corridors. In this paper, we propose a paradigm for planning in dynamic environments, called Disruption-Limited Planning (DLP), that allows a robot to disrupt the motions of dynamic obstacles in order to accomplish its task. DLP builds on the premise that while a robot may have to disrupt others’ trajectories to achieve its goals, it should try to limit the disruption. DLP assumes that it can estimate others’ response to its own actions/plans, and plans its own path while ensuring that no other agents’ disrupted trajectory cost gets worse than w-times their initial trajectory costs. While our formulation is motivated by the Stackelberg competitions, we show that DLP can be both more expressive and computationally more efficient compared to a Stackelberg planner. We present DLP paradigm, develop its efficient implementation based on A*, analyze its theoretical properties, and apply it to multiple planning in dynamic environment problems, including x, y, time planning, planning for self-driving, and planning for arm manipulation. We compare DLP with purely altruistic, purely egocentric, and optimal Stackelberg planners, demonstrating the efficacy of DLP over these alternatives.

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IROS Conference 2021 Conference Paper

Search-based Planning with Learned Behaviors for Navigation among Pedestrians

Ishani Chatterjee 0001
Yash Oza
Maxim Likhachev
Manuela Veloso

Agent control among pedestrians is often approached in one of the three following ways: using predefined behaviors for agent navigation, learning navigation behaviors from data, or search-based planning on a graph where each edge is a feasible action chosen from a set of predefined actions. While the first approach often produces natural looking motions and the second learns and utilizes complex interactions with pedestrians, both lack global reasoning about how to sequence these behaviors to achieve the overall goal. The third approach, namely search-based planning, does incorporate global reasoning but relies on predefined actions that do not involve any interactions with pedestrians or assume predefined interactions that cannot model complex interactions. This is a significant drawback since many situations such as going through a doorway blocked by other people require complex interactions in order to avoid highly suboptimal behaviors or not being able to get to the goal at all. To this end, we propose a search-based planning framework that constructs and searches a graph wherein each edge can be either a predefined action or a learned behavior. We further extend it to deal with the uncertainty arising from introducing learned behaviors. We present the algorithm, go over its theoretical analysis, and present experimental results.

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