7 citations · 19 across the 12 of their papers we have counts for
14 papers
Looped Actor: Depth-Recurrent Reasoning Models for Reinforcement Learning
T. Konstantin Rusch, Tim Seyde, Jared Boyer +2
Looped reasoning models repeatedly apply a shared set of parameters, enabling more computation without increasing the model size. These models also support input-dependent computat…
In-Place Tokenizer Expansion for Pre-trained LLMs
Jimmy T. H. Smith, Tarek Dakhran, Alberto Cabrera +7
A tokenizer fixed at the start of pre-training allocates vocabulary in proportion to the pre-training corpus, reflecting the deployment priorities at that time. When those prioriti…
Multi-Agent Robotic Control with Onboard Vision-Language Models
Kajetan Rachwał, Maciej Majek, Bartłomiej Boczek +6
Vision Language Models (VLMs) and Vision Language Action (VLA) models have shown promise in robotic control. Yet, they face significant challenges regarding explainability, general…
Zero-Overhead Introspection for Adaptive Test-Time Compute
Rohin Manvi, Joey Hong, Tim Seyde +3
Large language models excel at reasoning but lack key aspects of introspection, including anticipating their own success and the computation required to achieve it. Humans use real…
LFM2 Technical Report
Alexander Amini, Anna Banaszak, Harold Benoit +30
We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under…
Faster Algorithms for Growing Collision-Free Convex Polytopes in Robot Configuration Space
Peter Werner, Thomas Cohn, Rebecca H. Jiang +4
We propose two novel algorithms for constructing convex collision-free polytopes in robot configuration space. Finding these polytopes enables the application of stronger motion-pl…
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution
Tim Seyde, Peter Werner, Wilko Schwarting +2
Recent reinforcement learning approaches have shown surprisingly strong capabilities of bang-bang policies for solving continuous control benchmarks. The underlying coarse action s…
Towards Cooperative Flight Control Using Visual-Attention
Lianhao Yin, Makram Chahine, Tsun-Hsuan Wang +5
The cooperation of a human pilot with an autonomous agent during flight control realizes parallel autonomy. We propose an air-guardian system that facilitates cooperation between a…
Solving Continuous Control via Q-learning
Tim Seyde, Peter Werner, Wilko Schwarting +4
While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the as…
Interpreting Neural Policies with Disentangled Tree Representations
Tsun-Hsuan Wang, Wei Xiao, Tim Seyde +2
The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how…
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
Ryan Sander, Wilko Schwarting, Tim Seyde +3
Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents. Recent advances in experience replay propose using Mixup (Zhang et…
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Tim Seyde, Igor Gilitschenski, Wilko Schwarting +4
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space. In this work, we investigate the colloquially known…
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
Wilko Schwarting, Tim Seyde, Igor Gilitschenski +4
Learning competitive behaviors in multi-agent settings such as racing requires long-term reasoning about potential adversarial interactions. This paper presents Deep Latent Competi…
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles
Tim Seyde, Wilko Schwarting, Sertac Karaman +1
Learning complex robot behaviors through interaction requires structured exploration. Planning should target interactions with the potential to optimize long-term performance, whil…