4 citations · 12 across the 24 of their papers we have counts for
25 papers
ShieldVLA: Feasibility-Aware Safety Alignment for Vision-Language-Action Models
Manan Tayal, Akshay Nambi
Vision-Language-Action (VLA) models demonstrate strong generalization in robotic manipulation and navigation, but existing fine-tuning methods provide limited safety guarantees. Cu…
MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning
Manan Tayal
Robotic simulators are a cornerstone of modern research in aerial robotics, serving both as a vehicle for the development of new control algorithms and as the data source for train…
Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
Mumuksh Tayal, Manan Tayal, Ravi Prakash
Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints. Existing methods often rely on soft expected-cost ob…
Epigraph-Guided Flow Matching for Safe and Performant Offline Reinforcement Learning
Manan Tayal, Mumuksh Tayal
Offline reinforcement learning (RL) provides a compelling paradigm for training autonomous systems without the risks of online exploration, particularly in safety-critical domains.…
V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions
Mumuksh Tayal, Manan Tayal, Aditya Singh +2
Ensuring safety in autonomous systems requires controllers that aim to satisfy state-wise constraints without relying on online interaction.While existing Safe Offline RL methods t…
MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control
Manan Tayal, Aditya Singh, Shishir Kolathaya +1
Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-agent reinforcement learning (MARL), saf…
Safe and Performant Controller Synthesis using Gradient-based Model Predictive Control and Control Barrier Functions
Aditya Singh, Aastha Mishra, Manan Tayal +2
Ensuring both performance and safety is critical for autonomous systems operating in real-world environments. While safety filters such as Control Barrier Functions (CBFs) enforce…
Stochastic Neural Control Barrier Functions
Hongchao Zhang, Manan Tayal, Jackson Cox +3
Control Barrier Functions (CBFs) are utilized to ensure the safety of control systems. CBFs act as safety filters in order to provide safety guarantees without compromising system…
Neural Control Barrier Functions from Physics Informed Neural Networks
Shreenabh Agrawal, Manan Tayal, Aditya Singh +1
As autonomous systems become increasingly prevalent in daily life, ensuring their safety is paramount. Control Barrier Functions (CBFs) have emerged as an effective tool for guaran…
CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions
Manan Tayal, Aditya Singh, Pushpak Jagtap +1
Control Barrier Functions (CBFs) are a practical approach for designing safety-critical controllers, but constructing them for arbitrary nonlinear dynamical systems remains a chall…
RISE: Robust Imitation through Stochastic Encoding
Mumuksh Tayal, Manan Tayal, Ravi Prakash
Ensuring safety in robotic systems remains a fundamental challenge, especially when deploying offline policy-learning methods such as imitation learning in dynamic environments. Tr…
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
Manan Tayal, Aditya Singh, Shishir Kolathaya +1
As autonomous systems become more ubiquitous in daily life, ensuring high performance with guaranteed safety is crucial. However, safety and performance could be competing objectiv…
Semi-Supervised Safe Visuomotor Policy Synthesis using Barrier Certificates
Manan Tayal, Aditya Singh, Pushpak Jagtap +1
In modern robotics, addressing the lack of accurate state space information in real-world scenarios has led to a significant focus on utilizing visuomotor observation to provide sa…
Real Time Safety of Fixed-wing UAVs using Collision Cone Control Barrier Functions
Aryan Agarwal, Ravi Agrawal, Manan Tayal +2
Fixed-wing UAVs have transformed the transportation system with their high flight speed and long endurance, yet their safe operation in increasingly cluttered environments depends…
BiRoDiff: Diffusion policies for bipedal robot locomotion on unseen terrains
GVS Mothish, Manan Tayal, Shishir Kolathaya
Locomotion on unknown terrains is essential for bipedal robots to handle novel real-world challenges, thus expanding their utility in disaster response and exploration. In this wor…
Learning a Formally Verified Control Barrier Function in Stochastic Environment
Manan Tayal, Hongchao Zhang, Pushpak Jagtap +2
Safety is a fundamental requirement of control systems. Control Barrier Functions (CBFs) are proposed to ensure the safety of the control system by constructing safety filters or s…
A Collision Cone Approach for Control Barrier Functions
Manan Tayal, Bhavya Giri Goswami, Karthik Rajgopal +5
This work presents a unified approach for collision avoidance using Collision-Cone Control Barrier Functions (CBFs) in both ground (UGV) and aerial (UAV) unmanned vehicles. We prop…
Stoch BiRo: Design and Control of a low cost bipedal robot
GVS Mothish, Karthik Rajgopal, Ravi Kola +2
This paper introduces the Stoch BiRo, a cost-effective bipedal robot designed with a modular mechanical structure having point feet to navigate uneven and unfamiliar terrains. The…
Polygonal Cone Control Barrier Functions (PolyC2BF) for safe navigation in cluttered environments
Manan Tayal, Shishir Kolathaya
In fields such as mining, search and rescue, and archaeological exploration, ensuring real-time, collision-free navigation of robots in confined, cluttered environments is imperati…
Collision Cone Control Barrier Functions: Experimental Validation on UGVs for Kinematic Obstacle Avoidance
Bhavya Giri Goswami, Manan Tayal, Karthik Rajgopal +2
Autonomy advances have enabled robots in diverse environments and close human interaction, necessitating controllers with formal safety guarantees. This paper introduces an experim…
A Theoretical and Empirical Study on the Convergence of Adam with an "Exact" Constant Step Size in Non-Convex Settings
Alokendu Mazumder, Rishabh Sabharwal, Manan Tayal +2
In neural network training, RMSProp and Adam remain widely favoured optimisation algorithms. One of the keys to their performance lies in selecting the correct step size, which can…
Safe Legged Locomotion using Collision Cone Control Barrier Functions (C3BFs)
Manan Tayal, Shishir Kolathaya
Legged robots exhibit significant potential across diverse applications, including but not limited to hazardous environment search and rescue missions and the exploration of unexpl…
Control Barrier Functions in Dynamic UAVs for Kinematic Obstacle Avoidance: A Collision Cone Approach
Manan Tayal, Rajpal Singh, Jishnu Keshavan +1
Unmanned aerial vehicles (UAVs), specifically quadrotors, have revolutionized various industries with their maneuverability and versatility, but their safe operation in dynamic env…
Control Barrier Functions in UGVs for Kinematic Obstacle Avoidance: A Collision Cone Approach
Phani Thontepu, Bhavya Giri Goswami, Manan Tayal +6
In this paper, we propose a new class of Control Barrier Functions (CBFs) for Unmanned Ground Vehicles (UGVs) that help avoid collisions with kinematic (non-zero velocity) obstacle…