Ruchi Panwar

About

Ruchi Panwar is a robotics researcher whose work focuses on enhancing the stability, autonomy, and human-like motion of bipedal robots. Her primary research areas include biped robot locomotion, trajectory generation, neural network-based control, and vision-aided navigation. Panwar’s major contributions lie in developing algorithms that enable stable, human-like gait patterns, particularly through the integration of upper body motion and active toe joints, as demonstrated in her 2018 work on trajectory tracking using artificial neural networks, which has garnered 16 citations. She has also advanced the field of autonomous navigation by combining YOLO-based vision systems with reinforcement learning (Sarsa algorithm) to enable biped robots to safely navigate uncertain environments, such as hospitals, while avoiding obstacles and small objects. Her 2019 paper on unsupervised neural networks for inverse kinematics offers a novel solution to a classic robotics challenge, further showcasing her innovative approach. With a growing citation record and a focus on practical, real-world applications, Panwar is contributing to the next generation of intelligent, stable, and autonomous humanoid robots.

Research Focus

Key Achievements

Top Papers

1. 1
   Trajectory tracking using artificial neural network for stable human-like gait with upper body motion

   16 citations · 2018
2. 2
   Yolo Based Vision-Aided Obstacles Navigation and Avoidance with Path Planning using Sarsa Algorithm for Biped Robot in an Uncertain Hospital Environment

   4 citations · 2024
3. 3
   Effect of Upper Body Motion on Biped Robot Stability

   4 citations · 2018
4. 4
   An Unsupervised Neural Network Approach for Inverse Kinematics Solution of Manipulator following Kalman Filter based Trajectory

   3 citations · 2019
5. 5
   Stable polynomial gait of a biped robot with Toe joint

   2 citations · 2017

Key Collaborators

Contact & Links