Dvij Kalaria — 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 …

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2025

Agile Mobility with Rapid Online Adaptation via Meta-Learning and Uncertainty-Aware MPPI

Dvij Kalaria, Haoru Xue, Wenli Xiao, Tony Tao, Guanya Shi, John M. Dolan

ICRA 2025

Modern non-linear model-based controllers require an accurate physics model and model parameters to be able to control mobile robots at their limits. Also, due to surface slipping at high speeds, the friction parameters may continually change (like tire degradation in autonomous racing), and the con

Cited by 3 Source ↗Scholar ↗

2025

AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility

Wenli Xiao, Haoru Xue, Tony Tao, Dvij Kalaria, John M. Dolan, Guanya Shi

ICRA 2025

Recent works in the robot learning community have successfully introduced generalist models capable of controlling various robot embodiments across a wide range of tasks, such as navigation and locomotion. However, achieving agile control, which pushes the limits of robotic performance, still relies

Cited by 25 Source ↗Scholar ↗

2025

Disturbance Observer-based Control Barrier Functions with Residual Model Learning for Safe Reinforcement Learning

Dvij Kalaria, Qin Lin, John M. Dolan

IROS 2025

Reinforcement learning (RL) agents need to explore their environment to learn optimal behaviors and achieve maximum rewards. However, exploration can be risky when training RL directly on real systems, while simulation-based training introduces the tricky issue of the sim-to-real gap. Recent approac

Cited by 2 Source ↗Scholar ↗

2025

LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos

Daniel Etaat, Dvij Kalaria, Nima Rahmanian, S. Shankar Sastry

CVPR 2025poster

Physical agility is a necessary skill in competitive table tennis, but by no means sufficient. Champions excel in this fast-paced and highly dynamic environment by anticipating their opponent's intent - buying themselves the necessary time to react. In this work, we take one step towards designing s…

Cited by 0 Source ↗PDF ↗Scholar ↗

2024

Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine

Dvij Kalaria, Qin Lin, John M. Dolan

ICRA 2024poster

Autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times. Autonomous race cars require highly accurate perception, state estimation, planning, and control. Adding to this complexity is the need to accurately…

Cited by 12 Source ↗Scholar ↗

2022

Online Adaptive Compensation for Model Uncertainty Using Extreme Learning Machine-based Control Barrier Functions

Emanuel Munoz, Dvij Kalaria, Qin Lin, John M. Dolan

IROS 2022poster

A control barrier functions-based quadratic programming (CBF-QP) method has emerged as a controller synthesis tool to assure safety of autonomous systems owing to the appealing safe forward invariant set. However, the provable safety relies on a precisely described dynamic model, which is not always…

Cited by 4 Source ↗Scholar ↗