1 citations · 2 across the 9 of their papers we have counts for
9 papers
BLINK: Behavioral Latent Modeling of NK Cell Cytotoxicity
Iman Nematollahi, Jose Francisco Villena-Ossa, Alina Moter +6
Machine learning models of cellular interaction dynamics hold promise for understanding cell behavior. Natural killer (NK) cell cytotoxicity is a prominent example of such interact…
Spectral Alignment in Forward-Backward Representations via Temporal Abstraction
Seyed Mahdi B. Azad, Jasper Hoffmann, Iman Nematollahi +3
Forward-backward (FB) representations provide a powerful framework for learning the successor representation (SR) in continuous spaces by enforcing a low-rank factorization. Howeve…
DiWA: Diffusion Policy Adaptation with World Models
Akshay L Chandra, Iman Nematollahi, Chenguang Huang +3
Fine-tuning diffusion policies with reinforcement learning (RL) presents significant challenges. The long denoising sequence for each action prediction impedes effective reward pro…
LUMOS: Language-Conditioned Imitation Learning with World Models
Iman Nematollahi, Branton DeMoss, Akshay L Chandra +3
We introduce LUMOS, a language-conditioned multi-task imitation learning framework for robotics. LUMOS learns skills by practicing them over many long-horizon rollouts in the laten…
Bayesian Optimization for Sample-Efficient Policy Improvement in Robotic Manipulation
Adrian Röfer, Iman Nematollahi, Tim Welschehold +2
Sample efficient learning of manipulation skills poses a major challenge in robotics. While recent approaches demonstrate impressive advances in the type of task that can be addres…
Robot Skill Generalization via Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models
Iman Nematollahi, Kirill Yankov, Wolfram Burgard +1
A long-standing challenge for a robotic manipulation system operating in real-world scenarios is adapting and generalizing its acquired motor skills to unseen environments. We tack…
T3VIP: Transformation-based 3D Video Prediction
Iman Nematollahi, Erick Rosete-Beas, Seyed Mahdi B. Azad +3
For autonomous skill acquisition, robots have to learn about the physical rules governing the 3D world dynamics from their own past experience to predict and reason about plausible…
Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models
Iman Nematollahi, Erick Rosete-Beas, Adrian Röfer +3
A core challenge for an autonomous agent acting in the real world is to adapt its repertoire of skills to cope with its noisy perception and dynamics. To scale learning of skills t…
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction
Iman Nematollahi, Oier Mees, Lukas Hermann +1
A key challenge for an agent learning to interact with the world is to reason about physical properties of objects and to foresee their dynamics under the effect of applied forces.…