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Dive into the research topics where Ram Vasudevan is active.

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Featured researches published by Ram Vasudevan.


The International Journal of Robotics Research | 2014

Convex optimization of nonlinear feedback controllers via occupation measures

Anirudha Majumdar; Ram Vasudevan; Mark M. Tobenkin; Russ Tedrake

The construction of feedback control laws for underactuated nonlinear robotic systems with input saturation limits is crucial for dynamic robotic tasks such as walking, running, or flying. Existing techniques for feedback control design are either restricted to linear systems, rely on discretizations of the state space, or require solving a nonconvex optimization problem that requires feasible initialization. This paper presents a method for designing feedback controllers for polynomial systems that maximize the size of the time-limited backwards reachable set (BRS). In contrast to traditional approaches based on Lyapunov’s criteria for stability, we rely on the notion of occupation measures to pose this problem as an infinite-dimensional linear program which can then be approximated in finite dimension via semidefinite programs (SDPs). The solution to each SDP yields a polynomial control policy and an outer approximation of the largest achievable BRS which is well suited for use in a trajectory library or feedback motion planning algorithm. We demonstrate the efficacy and scalability of our approach on six nonlinear systems. Comparisons to an infinite-horizon linear quadratic regulator approach and an approach relying on Lyapunov’s criteria for stability are also included in order to illustrate the improved performance of the presented technique.


conference on decision and control | 2014

Convex computation of the reachable set for controlled polynomial hybrid systems

Victor Shia; Ram Vasudevan; Ruzena Bajcsy; Russ Tedrake

This paper presents an approach to computing the time-limited backwards reachable set (BRS) of a semialgebraic target set for controlled polynomial hybrid systems with semialgebraic state and input constraints. By relying on the notion of occupation measures, the computation of the BRS of a target set that may be distributed across distinct subsystems of the hybrid system, is posed as an infinite dimensional linear program (LP). Computationally tractable approximations to this LP are constructed via a sequence of semidefinite programs each of which is proven to construct an outer approximation of the true BRS with asymptotically vanishing conservatism. In contrast to traditional Lyapunov based approaches, the presented approach is convex and does not require any form of initialization. The performance of the presented algorithm is illustrated on 2 nonlinear controlled hybrid systems.


robotics: science and systems | 2013

Convex Optimization of Nonlinear Feedback Controllers via Occupation Measures.

Anirudha Majumdar; Ram Vasudevan; Mark M. Tobenkin; Russ Tedrake

In this paper, we present an approach for designing feedback controllers for polynomial systems that maximize the size of the time-limited backwards reachable set (BRS). We rely on the notion of occupation measures to pose the synthesis problem as an infinite dimensional linear program (LP) and provide finite dimensional approximations of this LP in terms of semidefinite programs (SDPs). The solution to each SDP yields a polynomial control policy and an outer approximation of the largest achievable BRS. In contrast to traditional Lyapunov based approaches which are non-convex and require feasible initialization, our approach is convex and does not require any form of initialization. The resulting time-varying controllers and approximated reachable sets are well-suited for use in a trajectory library or feedback motion planning algorithm. We demonstrate the efficacy and scalability of our approach on five nonlinear systems.


arXiv: Robotics | 2013

Technical Report: Convex Optimization of Nonlinear Feedback Controllers via Occupation Measures

Anirudha Majumdar; Ram Vasudevan; Mark M. Tobenkin; Russ Tedrake


6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems and Safety 2013, DSP 2013 | 2013

Probabilistic driver modeling to characterize human behavior for semiautonomous framework

Katherine Driggs Campbell; Victor Shia; Ram Vasudevan; Francesco Borrelli; Ruzena Bajcsy


international conference on robotics and automation | 2018

Force Generation by Parallel Combinations of Fiber-Reinforced Fluid-Driven Actuators

Daniel Bruder; Audrey Sedal; Ram Vasudevan; C. David Remy


arXiv: Robotics | 2018

Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environments.

Ming-Yuan Yu; Ram Vasudevan; Matthew Johnson-Roberson


arXiv: Robotics | 2018

Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction.

Xiaoxiao Du; Ram Vasudevan; Matthew Johnson-Roberson


arXiv: Robotics | 2018

Guaranteed Globally Optimal Planar Pose Graph and Landmark SLAM via Sparse-Bounded Sums-of-Squares Programming.

Joshua G. Mangelson; Jinsun Liu; Ryan M. Eustice; Ram Vasudevan


arXiv: Robotics | 2018

A constrained control-planning strategy for redundant manipulators

Corina Barbalata; Ram Vasudevan; Matthew Johnson-Roberson

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Russ Tedrake

Massachusetts Institute of Technology

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Anirudha Majumdar

Massachusetts Institute of Technology

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Mark M. Tobenkin

Massachusetts Institute of Technology

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Ruzena Bajcsy

University of California

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Victor Shia

University of California

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