324 citations · 420 across the 56 of their papers we have counts for
18 papers · 1 filter
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…
Real-Time Gait Adaptation for Quadrupeds using Model Predictive Control and Reinforcement Learning
Prakrut Kotecha, Ganga Nair B, Shishir Kolathaya
Model-free reinforcement learning (RL) has enabled adaptable and agile quadruped locomotion; however, policies often converge to a single gait, leading to suboptimal performance. T…
A Co-Design Framework for Energy-Aware Monoped Jumping with Detailed Actuator Modeling
Aman Singh, Aastha Mishra, Deepak Kapa +2
A monoped's jump height and energy consumption depend on both, its mechanical design and control strategy. Existing co-design frameworks typically optimize for either maximum heigh…
COMPAct: Computational Optimization and Automated Modular design of Planetary Actuators
Aman Singh, Deepak Kapa, Suryank Joshi +1
The optimal design of robotic actuators is a critical area of research, yet limited attention has been given to optimizing gearbox parameters and automating actuator CAD. This pape…
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…
Signal Temporal Logic Compliant Co-design of Planning and Control
Manas Sashank Juvvi, Tushar Dilip Kurne, Vaishnavi J +2
This work presents a novel co-design strategy that integrates trajectory planning and control to handle STL-based tasks in autonomous robots. The method consists of two phases: $(i…
Fixed time convergence guarantees for Higher Order Control Barrier Functions
Janani S K, Shishir Kolathaya
We present a novel method for designing higher-order Control Barrier Functions (CBFs) that guarantee convergence to a safe set within a user-specified finite. Traditional Higher Or…
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…
Safety Certification in the Latent space using Control Barrier Functions and World Models
Mehul Anand, Shishir Kolathaya
Synthesising safe controllers from visual data typically requires extensive supervised labelling of safety-critical data, which is often impractical in real-world settings. Recent…
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…
Comparison between External and Internal Single Stage Planetary gearbox actuators for legged robots
Aman Singh, Deepak Kapa, Prasham Chedda +1
Legged robots, such as quadrupeds and humanoids, require high-performance actuators for efficient locomotion. Quasi-Direct-Drive (QDD) actuators with single-stage planetary gearbox…
Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion
Prakrut Kotecha, Aditya Shirwatkar, Shishir Kolathaya
Lagrangian Neural Networks (LNNs) present a principled and interpretable framework for learning the system dynamics by utilizing inductive biases. While traditional dynamics models…
GRoQ-LoCO: Generalist and Robot-agnostic Quadruped Locomotion Control using Offline Datasets
Narayanan PP, Sarvesh Prasanth Venkatesan, Srinivas Kantha Reddy +1
Recent advancements in large-scale offline training have demonstrated the potential of generalist policy learning for complex robotic tasks. However, applying these principles to l…
MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion
Vamshi Kumar Kurva, Shishir Kolathaya
Quadrupedal robots are increasingly deployed for load-carrying tasks across diverse terrains. While Model Predictive Control (MPC)-based methods can account for payload variations,…
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…
A Chain-Driven, Sandwich-Legged Quadruped Robot: Design and Experimental Analysis
Aman Singh, Bhavya Giri Goswami, Ketan Nehete +1
This paper introduces a chain-driven, sandwich-legged mid-size quadruped robot designed as an accessible research platform. The design prioritizes enhanced locomotion, improved act…
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…