Times Square, NYC
ca. 2017

.

Hi,

I am a Ph.D. student at the University of Tsukuba, Japan, researching on Reinforcement Learning locomotion policies for humanoid robots at the CNRS-AIST Joint Robotics Lab (JRL).

I have been working at JRL since October, 2017, first as a full-time robotics engineer and then as an Research Assistant (RA) since April, 2019. All this time, I have been extremely fortunate to be mentored and advised by Fumio Kanehiro sensei.

If you are an acquaintance, a friend, a former coworker, or know me even remotely, I'm open to your (anonymous) feedback.

My Ph.D. research focusses on developing reinforcement learning based controllers for practical applications of real humanoid robots. This is challenging in several ways, some of which are due to the large size and high joint friction of robots like the HRP-5P.

For my master's, I worked on robotic perception and grasping - using CNNs and stacked-hourglass networks for 6-DoF pose estimation, with minimal human effort involved in data labelling and 3D modelling.

I'm also proud to have worked at UAS-DTU during my undergrad in New Delhi. It's a fun, yet an exceptionally passionate and hardworking group of student engineers. Go check them out!

mc-mujoco: Simulating Articulated Robots with FSM Controllers in MuJoCo
Rohan P. Singh, Pierre Gergondet, Fumio Kanehiro
IEEE/SICE International Symposium on System Integration (SII), 2023, USA
Jan. 2023

Learning Bipedal Walking On Planned Footsteps For Humanoid Robots
Rohan P. Singh, Mehdi Benallegue, Mitsuharu Morisawa, Rafael Cisneros, Fumio Kanehiro
IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), 2022, Japan
Dec. 2022

Rapid Pose Label Generation through Sparse Representation of Unknown Objects
Rohan P. Singh, Mehdi Benallegue, Yusuke Yoshiyasu, Fumio Kanehiro
International Conference on Robotics and Automation (ICRA), 2021, China (virtual)
Jun. 2021

Instance-specific 6-dof object pose estimation from minimal annotations
Rohan P. Singh, Iori Kumagai, Antonio Gabas, Mehdi Benallegue, Yusuke Yoshiyasu, Fumio Kanehiro
IEEE/SICE International Symposium on System Integration (SII), 2020, USA
Feb. 2020

Easy Pose Annotation of Real RGB-D Data for Known Objects
Rohan P. Singh, Mehdi Benallegue, Yusuke Yoshiyasu, Fumio Kanehiro
The Proceedings of JSME annual Conference on Robotics and Mechatronics (ROBOMECH) 2020, Japan (virtual)
May 2020

APE: A More Practical Approach To 6-Dof Pose Estimation
Antonio Gabas, Yusuke Yoshiyasu, Rohan Pratap Singh, Ryusuke Sagawa, Eiichi Yoshida
IEEE International Conference on Image Processing (ICIP), 2020, UAE (virtual)
Oct. 2020

• mc-mujoco
  Interface for mc-rtc with MuJoCo.
  This makes it very easy to simulate any robot with any controller as long as they can be expressed in mc-rtc.

• MuJoCo XML for JVRC1 / mujoco-python-viewer
  Open-source model files for the JVRC1 Humanoid Robot.
  This is a more realistic humanoid robot model than the one in OpenAI Gym.
  Show more...

  Install MuJoCo viewer, clone Git repository:

  
  	$ pip install mujoco-python-viewer
  	$ git clone https://github.com/isri-aist/jvrc_mj_description
  	

  Then, in Python:

  
  	import mujoco
  	import mujoco_viewer
  
  	model = mujoco.MjModel.from_xml_path('xml/jvrc1.xml')
  	data = mujoco.MjData(model)
  
  	# create the viewer object
  	viewer = mujoco_viewer.MujocoViewer(model, data)
  
  	# simulate and render
  	for _ in range(100000):
  	    mujoco.mj_step(model, data)
  	    viewer.render()
  
  	# close
  	viewer.close()
  	

• ANA Avatar XPRIZE
  I'm a member of Team Janus
  We are building teleoperated human-like Avatar robots!

• RapidPoseLabels
  Easily generate labeled data for 6-DoF Object Pose Estimation, even if you don't have a model
  Work published at ICRA, 2021. Contact me if you want help on how to use this!

• ObjectKeypointTrainer
  Train CNNs for Object Keypoint Detection (6-DoF Pose Estimation)
  Contact me if you want help on how to use this!