3 citations · 4 across the 9 of their papers we have counts for
10 papers
Kine2Go: Kinematic dataset for the Unitree Go2 robot with diverse gaits and motions
Władysław Pałucki, Paweł Siwak, Krzysztof Ciebiera +1
The recent popularity of robotics, combined with the steadily decreasing cost of robotic hardware, has lowered the entry barrier to robotics research and enabled rapid advancements…
Swim2Real: VLM-Guided System Identification for Sim-to-Real Transfer
Kevin Qiu, Kyle Walker, Mike Y. Michelis +2
We present Swim2Real, a pipeline that calibrates a 16-parameter robotic fish simulator from swimming videos using vision-language model (VLM) feedback, requiring no hand-designed s…
Reward-Conditioned Reinforcement Learning
Michal Nauman, Marek Cygan, Pieter Abbeel
Single-task RL agents are typically trained under a fixed reward function, which limits their robustness to reward misspecification and their ability to adapt to changing preferenc…
Vid2Sid: Videos Can Help Close the Sim2Real Gap
Kevin Qiu, Yu Zhang, Marek Cygan +1
Calibrating a robot simulator's physics parameters (friction, damping, material stiffness) to match real hardware is often done by hand or with black-box optimizers that reduce err…
Debate2Create: Robot Co-design via Multi-Agent LLM Debate
Kevin Qiu, Marek Cygan
We introduce Debate2Create (D2C), a multi-agent LLM framework that formulates robot co-design as structured, iterative debate grounded in physics-based evaluation. A design agent a…
Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners
Michal Nauman, Marek Cygan, Carmelo Sferrazza +2
Recent advances in language modeling and vision stem from training large models on diverse, multi-task data. This paradigm has had limited impact in value-based reinforcement learn…
Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control
Michal Nauman, Mateusz Ostaszewski, Krzysztof Jankowski +2
Sample efficiency in Reinforcement Learning (RL) has traditionally been driven by algorithmic enhancements. In this work, we demonstrate that scaling can also lead to substantial i…
A Case for Validation Buffer in Pessimistic Actor-Critic
Michal Nauman, Mateusz Ostaszewski, Marek Cygan
In this paper, we investigate the issue of error accumulation in critic networks updated via pessimistic temporal difference objectives. We show that the critic approximation error…
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning
Michal Nauman, Michał Bortkiewicz, Piotr Miłoś +3
Recent advancements in off-policy Reinforcement Learning (RL) have significantly improved sample efficiency, primarily due to the incorporation of various forms of regularization t…
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics
Michal Nauman, Marek Cygan
Risk-aware Reinforcement Learning (RL) algorithms like SAC and TD3 were shown empirically to outperform their risk-neutral counterparts in a variety of continuous-action tasks. How…