4 citations · 7 across the 6 of their papers we have counts for
7 papers
When One Step Is Enough: An Empirical Study of Euler Integration in Flow-Matching Policies
Shivakanth Sujit, Luca Nunziante, Rousslan Fernand Julien Dossa +1
Flow matching is now the dominant approach for vision-language action (VLA) robot policies, thanks to its expressiveness and simple training objective. However, inference for stand…
Improving Low-Cost Teleoperation: Augmenting GELLO with Force
Shivakanth Sujit, Luca Nunziante, Dan Ogawa Lillrank +2
In this work we extend the low-cost GELLO teleoperation system, initially designed for joint position control, with additional force information. Our first extension is to implemen…
Reinforcement Learning for Sequence Design Leveraging Protein Language Models
Jithendaraa Subramanian, Shivakanth Sujit, Niloy Irtisam +4
Protein sequence design, determined by amino acid sequences, are essential to protein engineering problems in drug discovery. Prior approaches have resorted to evolutionary strateg…
PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen +11
Goal-conditioned planning benefits from learned low-dimensional representations of rich observations. While compact latent representations typically learned from variational autoen…
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Shivakanth Sujit, Pedro H. M. Braga, Jorg Bornschein +1
Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challen…
Learning Robust Dynamics through Variational Sparse Gating
Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi +3
Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-eff…
Prioritizing Samples in Reinforcement Learning with Reducible Loss
Shivakanth Sujit, Somjit Nath, Pedro H. M. Braga +1
Most reinforcement learning algorithms take advantage of an experience replay buffer to repeatedly train on samples the agent has observed in the past. Not all samples carry the sa…