Search Results for author: Sanjiban Choudhury

Found 18 papers, 3 papers with code

Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics

no code implementations2 Apr 2021 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.

Imitation Learning Platform

Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap

1 code implementation4 Mar 2021 Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

We provide a unifying view of a large family of previous imitation learning algorithms through the lens of moment matching.

Imitation Learning

Feedback in Imitation Learning: The Three Regimes of Covariate Shift

no code implementations4 Feb 2021 Jonathan Spencer, Sanjiban Choudhury, Arun Venkatraman, Brian Ziebart, J. Andrew Bagnell

The learner often comes to rely on features that are strongly predictive of decisions, but are subject to strong covariate shift.

Causal Inference Decision Making +1

Blending MPC & Value Function Approximation for Efficient Reinforcement Learning

no code implementations ICLR 2021 Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots

We further propose an algorithm that changes $\lambda$ over time to reduce the dependence on MPC as our estimates of the value function improve, and test the efficacy our approach on challenging high-dimensional manipulation tasks with biased models in simulation.

Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making

no code implementations15 Oct 2019 Rogerio Bonatti, Wenshan Wang, Cherie Ho, Aayush Ahuja, Mirko Gschwindt, Efe Camci, Erdal Kayacan, Sanjiban Choudhury, Sebastian Scherer

In this work, we address the problem in its entirety and propose a complete system for real-time aerial cinematography that for the first time combines: (1) vision-based target estimation; (2) 3D signed-distance mapping for occlusion estimation; (3) efficient trajectory optimization for long time-horizon camera motion; and (4) learning-based artistic shot selection.

Decision Making Motion Capture +1

Leveraging Experience in Lazy Search

no code implementations16 Jul 2019 Mohak Bhardwaj, Sanjiban Choudhury, Byron Boots, Siddhartha Srinivasa

If new search problems are sufficiently similar to problems solved during training, the learned policy will choose a good edge evaluation ordering and solve the motion planning problem quickly.

Imitation Learning Motion Planning

Imitation Learning as $f$-Divergence Minimization

no code implementations30 May 2019 Liyiming Ke, Sanjiban Choudhury, Matt Barnes, Wen Sun, Gilwoo Lee, Siddhartha Srinivasa

We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes.

Imitation Learning

Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments

no code implementations4 Apr 2019 Rogerio Bonatti, Cherie Ho, Wenshan Wang, Sanjiban Choudhury, Sebastian Scherer

In this work, we overcome such limitations and propose a complete system for aerial cinematography that combines: (1) a vision-based algorithm for target localization; (2) a real-time incremental 3D signed-distance map algorithm for occlusion and safety computation; and (3) a real-time camera motion planner that optimizes smoothness, collisions, occlusions and artistic guidelines.

Motion Capture Platform +1

Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive Markov Decision Processes

no code implementations6 Oct 2018 Gilwoo Lee, Sanjiban Choudhury, Brian Hou, Siddhartha S. Srinivasa

We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge.

Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming

no code implementations28 Aug 2018 Rogerio Bonatti, yanfu Zhang, Sanjiban Choudhury, Wenshan Wang, Sebastian Scherer

Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with the capability of high-end film studios.

Hindsight is Only 50/50: Unsuitability of MDP based Approximate POMDP Solvers for Multi-resolution Information Gathering

no code implementations7 Apr 2018 Sankalp Arora, Sanjiban Choudhury, Sebastian Scherer

The contribution of the paper helps identify the properties of a POMDP problem for which the use of MDP based POMDP solvers is inappropriate, enabling better design choices.

Decision Making Imitation Learning

Bayesian Active Edge Evaluation on Expensive Graphs

no code implementations20 Nov 2017 Sanjiban Choudhury, Siddhartha Srinivasa, Sebastian Scherer

We are interested in planning algorithms that actively infer the underlying structure of the valid configuration space during planning in order to find solutions with minimal effort.

Active Learning Motion Planning

Anytime Motion Planning on Large Dense Roadmaps with Expensive Edge Evaluations

1 code implementation10 Nov 2017 Shushman Choudhury, Oren Salzman, Sanjiban Choudhury, Christopher M. Dellin, Siddhartha S. Srinivasa

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 computationally expensive.


Learning Heuristic Search via Imitation

no code implementations10 Jul 2017 Mohak Bhardwaj, Sanjiban Choudhury, Sebastian Scherer

In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand.

Motion Planning

Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs

1 code implementation NeurIPS 2017 Sanjiban Choudhury, Shervin Javdani, Siddhartha Srinivasa, Sebastian Scherer

By leveraging this property, we are able to significantly reduce computational complexity from exponential to linear in the number of edges.


Learning to Gather Information via Imitation

no code implementations13 Nov 2016 Sanjiban Choudhury, Ashish Kapoor, Gireeja Ranade, Debadeepta Dey

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 across a wide range of applications in autonomous exploration and inspection with mobile robots.

Imitation Learning

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