1 citations · 1 across the 7 of their papers we have counts for
10 papers
FLOORA: A Human-Aligned Domain-Specific Language Model for Architectural Design
Sahand Rezaei-Shoshtari, Patryk Wozniczka, Shu Ishida +14
Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly. We introduce FLOORA (Floor La…
Graph of Agents: Principled Long Context Modeling by Emergent Multi-Agent Collaboration
Taejong Joo, Shu Ishida, Ivan Sosnovik +4
As a model-agnostic approach to long context modeling, multi-agent systems can process inputs longer than a large language model's context window without retraining or architectura…
Fairness in Reinforcement Learning with Bisimulation Metrics
Sahand Rezaei-Shoshtari, Hanna Yurchyk, Scott Fujimoto +2
Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environments. By maximizing their reward without co…
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao +2
Reinforcement learning (RL) on high-dimensional and complex problems relies on abstraction for improved efficiency and generalization. In this paper, we study abstraction in the co…
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois Robert Hogan +2
In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal…
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Sahand Rezaei-Shoshtari, Rosie Zhao, Prakash Panangaden +2
Abstraction has been widely studied as a way to improve the efficiency and generalization of reinforcement learning algorithms. In this paper, we study abstraction in the continuou…
Learning Intuitive Physics with Multimodal Generative Models
Sahand Rezaei-Shoshtari, Francois Robert Hogan, Michael Jenkin +2
Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelligent and anticipatory actions. This pape…
Seeing Through your Skin: Recognizing Objects with a Novel Visuotactile Sensor
Francois Robert Hogan, Michael Jenkin, Sahand Rezaei-Shoshtari +3
We introduce a new class of vision-based sensor and associated algorithmic processes that combine visual imaging with high-resolution tactile sending, all in a uniform hardware and…
Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference
Sahand Rezaei-Shoshtari, David Meger, Inna Sharf
Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work. The targeted application is of a robotic manipulator…
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning
Sahand Rezaei-Shoshtari, David Meger, Inna Sharf
Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators. This approach l…