I am a 2nd year Robotics PhD student. My research is advised by Dr. Abdeslam Boularias and Dr. Kostas E. Bekris. I’m interested in Long-Horizon Task and Motion Planning and Scene Estimation for Robotics.
Publications:
2025 |
Ramesh, D; Keskar, S; Sivaramakrishnan, A; Bekris, K; Yu, J; Boularias, A PROBE: Proprioceptive Obstacle Detection and Estimation while Navigating in Clutter Conference IEEE International Conference on Robotics and Automation (ICRA), 2025. @conference{metha2025probe, title = {PROBE: Proprioceptive Obstacle Detection and Estimation while Navigating in Clutter}, author = {D Ramesh and S Keskar and A Sivaramakrishnan and K Bekris and J Yu and A Boularias}, url = {https://dhruvmetha.github.io/legged-probe/}, year = {2025}, date = {2025-05-01}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, abstract = {In critical applications, including search-andrescue in degraded environments, blockages can be prevalent and prevent the effective deployment of certain sensing modalities, particularly vision, due to occlusion and the constrained range of view of onboard camera sensors. To enable robots to tackle these challenges, we propose a new approach, Proprioceptive Obstacle Detection and Estimation while navigating in clutter (PROBE), which instead relies only on the robot's proprioception to infer the presence or the absence of occluded rectangular obstacles while predicting their dimensions and poses in SE(2). The approach is a Transformer neural network that receives as input a history of applied torques and sensed whole-body movements of the robot and returns a parameterized representation of the obstacles in the environment. The effectiveness of PROBE is evaluated on simulated environments in Isaac Gym and with a real Unitree Go1 quadruped robot.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In critical applications, including search-andrescue in degraded environments, blockages can be prevalent and prevent the effective deployment of certain sensing modalities, particularly vision, due to occlusion and the constrained range of view of onboard camera sensors. To enable robots to tackle these challenges, we propose a new approach, Proprioceptive Obstacle Detection and Estimation while navigating in clutter (PROBE), which instead relies only on the robot's proprioception to infer the presence or the absence of occluded rectangular obstacles while predicting their dimensions and poses in SE(2). The approach is a Transformer neural network that receives as input a history of applied torques and sensed whole-body movements of the robot and returns a parameterized representation of the obstacles in the environment. The effectiveness of PROBE is evaluated on simulated environments in Isaac Gym and with a real Unitree Go1 quadruped robot. |
Marougkas, I; Ramesh, D; Doerr, J; Granados, E; Sivaramakrishnan, A; Boularias, A; Bekris, K Integrating Model-based Control and RL for Sim2Real Transfer of Tight Insertion Policies Conference IEEE International Conference on Robotics and Automation (ICRA), 2025. @conference{marougkas2025integration, title = {Integrating Model-based Control and RL for Sim2Real Transfer of Tight Insertion Policies}, author = {I Marougkas and D Ramesh and J Doerr and E Granados and A Sivaramakrishnan and A Boularias and K Bekris}, year = {2025}, date = {2025-05-01}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, abstract = {Object insertion under tight tolerances (<1mm) is an important but challenging assembly task as even slight errors can result in undesirable contacts. Recent efforts have focused on using Reinforcement Learning (RL) and often depend on careful definition of dense reward functions. This work proposes an effective strategy for such tasks that integrates traditional model-based control with RL to achieve improved accuracy given training of the policy exclusively in simulation and zero- shot transfer to the real system. It employs a potential field- based controller to acquire a model-based policy for inserting a plug into a socket given full observability in simulation. This policy is then integrated with a residual RL one, which is trained in simulation given only a sparse, goal-reaching reward. A curriculum scheme over observation noise and action magnitude is used for training the residual RL policy. Both policy components use as input the SE(3) poses of both the plug and the socket and return the plug's SE(3) pose transform, which is executed by a robotic arm using a controller. The integrated policy is deployed on the real system without further training or fine-tuning, given a visual SE(3) object tracker. The proposed solution and alternatives are evaluated across a variety of objects and conditions in simulation and reality. The proposed approach outperforms recent RL methods in this domain and prior efforts for hybrid policies. Ablations highlight the impact of each component of the approach}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Object insertion under tight tolerances (<1mm) is an important but challenging assembly task as even slight errors can result in undesirable contacts. Recent efforts have focused on using Reinforcement Learning (RL) and often depend on careful definition of dense reward functions. This work proposes an effective strategy for such tasks that integrates traditional model-based control with RL to achieve improved accuracy given training of the policy exclusively in simulation and zero- shot transfer to the real system. It employs a potential field- based controller to acquire a model-based policy for inserting a plug into a socket given full observability in simulation. This policy is then integrated with a residual RL one, which is trained in simulation given only a sparse, goal-reaching reward. A curriculum scheme over observation noise and action magnitude is used for training the residual RL policy. Both policy components use as input the SE(3) poses of both the plug and the socket and return the plug's SE(3) pose transform, which is executed by a robotic arm using a controller. The integrated policy is deployed on the real system without further training or fine-tuning, given a visual SE(3) object tracker. The proposed solution and alternatives are evaluated across a variety of objects and conditions in simulation and reality. The proposed approach outperforms recent RL methods in this domain and prior efforts for hybrid policies. Ablations highlight the impact of each component of the approach |