45 citations · 194 across the 38 of their papers we have counts for
21 papers · 1 filter
Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought
Huaxiaoyue Wang, Gonzalo Gonzalez-Pumariega, Yash Sharma +1
Language instructions and demonstrations are two natural ways for users to teach robots personalized tasks. Recent progress in Large Language Models (LLMs) has shown impressive per…
Leveraging Experience in Lazy Search
Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots +1
Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the…
Guided Incremental Local Densification for Accelerated Sampling-based Motion Planning
Aditya Mandalika, Rosario Scalise, Brian Hou +2
Sampling-based motion planners rely on incremental densification to discover progressively shorter paths. After computing feasible path between start and goal , the…
Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics
Matthew Schmittle, Sanjiban Choudhury, Siddhartha S. Srinivasa
A key challenge in Imitation Learning (IL) is that optimal state actions demonstrations are difficult for the teacher to provide. For example in robotics, providing kinesthetic dem…
Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges
Brian Hou, Sanjiban Choudhury, Gilwoo Lee +2
Collision checking is a computational bottleneck in motion planning, requiring lazy algorithms that explicitly reason about when to perform this computation. Optimism in the face o…
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts
Gilwoo Lee, Brian Hou, Sanjiban Choudhury +1
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people. We formulate this as Bayesian Reinforcement Lear…
Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making
Rogerio Bonatti, Wenshan Wang, Cherie Ho +6
Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone wh…
MuSHR: A Low-Cost, Open-Source Robotic Racecar for Education and Research
Siddhartha S. Srinivasa, Patrick Lancaster, Johan Michalove +8
We present MuSHR, the Multi-agent System for non-Holonomic Racing. MuSHR is a low-cost, open-source robotic racecar platform for education and research, developed by the Personal R…
LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning
Rahul Kumar, Aditya Mandalika, Sanjiban Choudhury +1
We consider the problem of leveraging prior experience to generate roadmaps in sampling-based motion planning. A desirable roadmap is one that is sparse, allowing for fast search,…
Leveraging Experience in Lazy Search
Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots +1
Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the…
Generalized Lazy Search for Robot Motion Planning: Interleaving Search and Edge Evaluation via Event-based Toggles
Aditya Mandalika, Sanjiban Choudhury, Oren Salzman +1
Lazy search algorithms can efficiently solve problems where edge evaluation is the bottleneck in computation, as is the case for robotic motion planning. The optimal algorithm in t…
Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments
Rogerio Bonatti, Cherie Ho, Wenshan Wang +2
The use of drones for aerial cinematography has revolutionized several applications and industries that require live and dynamic camera viewpoints such as entertainment, sports, an…
Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming
Rogerio Bonatti, Yanfu Zhang, Sanjiban Choudhury +2
Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with th…
Bayesian Active Edge Evaluation on Expensive Graphs
Sanjiban Choudhury, Siddhartha Srinivasa, Sebastian Scherer
Robots operate in environments with varying implicit structure. For instance, a helicopter flying over terrain encounters a very different arrangement of obstacles than a robotic a…
Data-driven Planning via Imitation Learning
Sanjiban Choudhury, Mohak Bhardwaj, Sankalp Arora +4
Robot planning is the process of selecting a sequence of actions that optimize for a task specific objective. The optimal solutions to such tasks are heavily influenced by the impl…
Anytime Motion Planning on Large Dense Roadmaps with Expensive Edge Evaluations
Shushman Choudhury, Oren Salzman, Sanjiban Choudhury +2
We propose an algorithmic framework for efficient anytime motion planning on large dense geometric roadmaps, in domains where collision checks and therefore edge evaluations are co…
Learning Heuristic Search via Imitation
Mohak Bhardwaj, Sanjiban Choudhury, Sebastian Scherer
Robotic motion planning problems are typically solved by constructing a search tree of valid maneuvers from a start to a goal configuration. Limited onboard computation and real-ti…
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
Sanjiban Choudhury, Shervin Javdani, Siddhartha Srinivasa +1
Robotic motion-planning problems, such as a UAV flying fast in a partially-known environment or a robot arm moving around cluttered objects, require finding collision-free paths qu…
Adaptive Information Gathering via Imitation Learning
Sanjiban Choudhury, Ashish Kapoor, Gireeja Ranade +2
In the adaptive information gathering problem, a policy is required to select an informative sensing location using the history of measurements acquired thus far. While there is an…
Learning to Gather Information via Imitation
Sanjiban Choudhury, Ashish Kapoor, Gireeja Ranade +1
The budgeted information gathering problem - where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world - appears in practice…
Densification Strategies for Anytime Motion Planning over Large Dense Roadmaps
Shushman Choudhury, Oren Salzman, Sanjiban Choudhury +1
We consider the problem of computing shortest paths in a dense motion-planning roadmap . We assume that~, the number of vertices of , is very large. Th…