1.2k citations · 3.1k across the 91 of their papers we have counts for
14 papers · 1 filter
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
Samuel Ainsworth, Matt Barnes, Siddhartha Srinivasa
In many environments, only a relatively small subset of the complete state space is necessary in order to accomplish a given task. We develop a simple technique using emergency sto…
On the Convergence of the Iterative Linear Exponential Quadratic Gaussian Algorithm to Stationary Points
Vincent Roulet, Maryam Fazel, Siddhartha Srinivasa +1
A classical method for risk-sensitive nonlinear control is the iterative linear exponential quadratic Gaussian algorithm. We present its convergence analysis from a first-order opt…
Towards Effective Human-AI Teams: The Case of Collaborative Packing
Gilwoo Lee, Christoforos Mavrogiannis, Siddhartha S. Srinivasa
We focus on the problem of designing an artificial agent (AI), capable of assisting a human user to complete a task. Our goal is to guide human users towards optimal task performan…
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy +1
We examine popular gradient-based algorithms for nonlinear control in the light of the modern complexity analysis of first-order optimization algorithms. The examination reveals th…
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…
Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items
Ethan K. Gordon, Xiang Meng, Matt Barnes +2
A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items. It must adapt to changing user food preferences under uncertain visual and phy…
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…
Robot-Assisted Feeding: Generalizing Skewering Strategies across Food Items on a Realistic Plate
Ryan Feng, Youngsun Kim, Gilwoo Lee +5
A robot-assisted feeding system must successfully acquire many different food items. A key challenge is the wide variation in the physical properties of food, demanding diverse acq…
Imitation Learning as -Divergence Minimization
Liyiming Ke, Sanjiban Choudhury, Matt Barnes +3
We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any…
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
Liyiming Ke, Xiujun Li, Yonatan Bisk +6
We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the Room-to-Room (R2R) V…
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…
Learning Configuration Space Belief Model from Collision Checks for Motion Planning
Sumit Kumar, Shushman Choudhary, Siddhartha Srinivasa
For motion planning in high dimensional configuration spaces, a significant computational bottleneck is collision detection. Our aim is to reduce the expected number of collision c…
The Assistive Multi-Armed Bandit
Lawrence Chan, Dylan Hadfield-Menell, Siddhartha Srinivasa +1
Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are a…