Shankar Sastry — accepted AI-conference papers

2026

DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction Via Guided Diffusion

Dvij Kalaria, Sudarshan S Harithas, Pushkal Katara, Sangkyung Kwak, Sarthak Bhagat, Shankar Sastry +4

ICRA 2026poster

We introduce DreamControl, a novel methodology for learning autonomous whole-body humanoid skills. DreamControl leverages the strengths of diffusion models and Reinforcement Learning (RL): our core innovation is the use of a diffusion prior trained on human motion data, which subsequently guides an …

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2026

HITTER: A HumanoId Table TEnnis Robot Via Hierarchical Planning and Learning

Zhi Su, Bike Zhang, Nima Abraham Rahmanian, Yuman Gao, Qiayuan Liao, Caitlin Regan +2

ICRA 2026poster

Humanoid robots have recently achieved impressive progress in locomotion and whole-body control, yet they remain constrained in tasks that demand rapid interaction with dynamic environments through manipulation. Table tennis exemplifies such a challenge: with ball speeds exceeding 5 m/s, players mus…

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2026

Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer

Haoru Xue, Tairan He, Zi Wang, Qingwei Ben, Wenli Xiao, Zhengyi Luo +6

CVPR 2026

Recent progress in GPU-accelerated, photorealistic simulation has opened a scalable data-generation path for robot learning, where massive physics and visual randomization allow policies to generalize beyond curated environments. Building on these advances, we develop a teacher-student-bootstrap lea

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2026

VIRAL: Visual Sim-to-Real at Scale for Humanoid Loco-Manipulation

Tairan He, Zi Wang, Haoru Xue, Qingwei Ben, Zhengyi Luo, Wenli Xiao +8

CVPR 2026

A key barrier to the real-world deployment of humanoid robots is the lack of autonomous loco-manipulation skills. We introduce VIRAL, a visual sim-to-real framework that learns humanoid loco-manipulation entirely in simulation and deploys it zero-shot to real hardware. VIRAL follows a teacher-studen

Cited by 0 Source ↗code ↗Scholar ↗

2022

Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets

Chinmay Maheshwari, Shankar Sastry, Eric Mazumdar

NeurIPS 2022accept

We study the problem of online learning in competitive settings in the context of two-sided matching markets. In particular, one side of the market, the agents, must learn about their preferences over the other side, the firms, through repeated interaction while competing with other agents for succe…

Cited by 19 Source ↗PDF ↗Scholar ↗

2022

Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning

Tyler Westenbroek, Fernando Castaneda, Ayush Agrawal, Shankar Sastry, Koushil Sreenath

CoRL 2022poster

Recent advances in the reinforcement learning (RL) literature have enabled roboticists to automatically train complex policies in simulated environments. However, due to the poor sample complexity of these methods, solving RL problems using real-world data remains a challenging problem. This paper i…

Cited by 28 Source ↗Scholar ↗

2022

Simultaneous Localization and Mapping: Through the Lens of Nonlinear Optimization

Amay Saxena, Chih-Yuan Chiu, Ritika Shrivastava, Joseph Menke, Shankar Sastry

RA-L 2022

Simultaneous Localization and Mapping (SLAM) algorithms perform visual-inertial estimation via filtering or batch optimization methods. Empirical evidence suggests that filtering algorithms are computationally faster, while optimization methods are more accurate. This work presents an optimization-b

Cited by 7 Source ↗Scholar ↗

2022

Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization

Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, Shankar Sastry, Lillian Ratliff

AISTATS 2022poster

Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data. We propose the random reshuffling-based gradient-free Optimistic Gradient Descent-Ascent algorithm for solving convex-concave min-max problems with finite sum s…

Cited by 22 Source ↗PDF ↗Scholar ↗

2021

Who Leads and Who Follows in Strategic Classification?

Tijana Zrnic, Eric Mazumdar, Shankar Sastry, Michael Jordan

NeurIPS 2021poster

As predictive models are deployed into the real world, they must increasingly contend with strategic behavior. A growing body of work on strategic classification treats this problem as a Stackelberg game: the decision-maker "leads" in the game by deploying a model, and the strategic agents "follow"…

Cited by 68 Source ↗PDF ↗Scholar ↗

2018

Step Size Matters in Deep Learning

Kamil Nar, Shankar Sastry

NeurIPS 2018spotlight

Training a neural network with the gradient descent algorithm gives rise to a discrete-time nonlinear dynamical system. Consequently, behaviors that are typically observed in these systems emerge during training, such as convergence to an orbit but not to a fixed point or dependence of convergence o…

Cited by 42 Source ↗PDF ↗code ↗Scholar ↗

2017

Active Preference-Based Learning of Reward Functions

Dorsa Sadigh, Anca Dragan, Shankar Sastry, Sanjit Seshia

RSS 2017poster

Our goal is to efficiently learn reward functions encoding a human's preferences for how a dynamical system should act. There are two challenges with this. First, in many problems it is difficult for people to provide demonstrations of the desired system trajectory (like a high-DOF robot arm motion…

Cited by 453 Source ↗PDF ↗Scholar ↗

2017

Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning

Joshua Achiam, Shankar Sastry

ICLR 2017workshop

Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have been achieved mostly using simple heuristic exploration strategies such as $\epsilon$-greedy action selection or Gaussian c…

Cited by 301 Source ↗Scholar ↗

2016

Planning for Autonomous Cars that Leverage Effects on Human Actions

Dorsa Sadigh, Shankar Sastry, Sanjit A. Seshia, Anca D. Dragan

RSS 2016poster

Traditionally, autonomous cars make predic- tions about other driversâ future trajectories, and plan to stay out of their way. This tends to result in defensive and opaque behaviors. Our key insight is that an autonomous carâs actions will actually affect what other cars will do in response, whe…

Cited by 661 Source ↗PDF ↗Scholar ↗