6 citations · 7 across the 11 of their papers we have counts for
12 papers
Model-Based Online Decision Making via Generative Trajectory Planning
Haldun Balim, Yilun Du, Na Li
Online decision-making faces a tension between efficient inference and long-horizon reasoning. Value-based methods rely on short-horizon bootstrapping that limits the propagation o…
A Single Diffusion-Policy Controller for Multi-Task Block Pushing with Zero-Shot Sim-to-Real Transfer
Haitong Ma, Haldun Balim, Yang Hu +2
Diffusion policies have shown promising empirical performance in representing and learning complex maneuvers for robots using behavior cloning (BC). In this paper, we explore train…
Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer
Aryan Naveen, Haitong Ma, Haldun Balim +1
Reinforcement learning has achieved remarkable success in learning complex control policies, yet its applicability remains limited due to sample inefficiency and poor generalizatio…
Decentralized Diffusion Policy Learning for Enhanced Exploration in Cooperative Multi-agent Reinforcement Learning
Yuyang Zhang, Haldun Balim, Na Li
Cooperative multi-agent reinforcement learning (MARL) involves complex agent interactions and requires effective exploration strategies. A prominent class of MARL algorithms, decen…
Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making
Haldun Balim, Na Li, Yilun Du
Offline decision-making via diffusion models often produces trajectories that are misaligned with system dynamics, limiting their reliability for control. We propose Model Predicti…
Flexible Locomotion Learning with Diffusion Model Predictive Control
Runhan Huang, Haldun Balim, Heng Yang +1
Legged locomotion demands controllers that are both robust and adaptable, while remaining compatible with task and safety considerations. However, model-free reinforcement learning…
A Model-Based Approach to Imitation Learning through Multi-Step Predictions
Haldun Balim, Yang Hu, Yuyang Zhang +1
Imitation learning is a widely used approach for training agents to replicate expert behavior in complex decision-making tasks. However, existing methods often struggle with compou…
Stochastic Data-Driven Predictive Control: Chance-Constraint Satisfaction with Identified Multi-step Predictors
Haldun Balim, Andrea Carron, Melanie N. Zeilinger +1
We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predicto…
From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements
Haldun Balim, Andrea Carron, Melanie N. Zeilinger +1
We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data. The proposed framework is designed for discrete-…
Can Transformers Learn Optimal Filtering for Unknown Systems?
Haldun Balim, Zhe Du, Samet Oymak +1
Transformer models have shown great success in natural language processing; however, their potential remains mostly unexplored for dynamical systems. In this work, we investigate t…
Koopman-inspired Implicit Backward Reachable Sets for Unknown Nonlinear Systems
Haldun Balim, Antoine Aspeel, Zexiang Liu +1
Koopman liftings have been successfully used to learn high dimensional linear approximations for autonomous systems for prediction purposes, or for control systems for leveraging l…
EFE: End-to-end Frame-to-Gaze Estimation
Haldun Balim, Seonwook Park, Xi Wang +2
Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as…