324 citations · 420 across the 56 of their papers we have counts for
13 papers · 2 filters
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…
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…
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…
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…