6 citations · 28 across the 20 of their papers we have counts for
24 papers
When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning
Lakshita Dodeja, Ondrej Biza, Shivam Vats +5
Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning. However, BC lacks a self-guided mechanism for online improvement after demonstrations have been…
One-Shot Cross-Geometry Skill Transfer through Part Decomposition
Skye Thompson, Ondrej Biza, George Konidaris
Given a demonstration, a robot should be able to generalize a skill to any object it encounters-but existing approaches to skill transfer often fail to adapt to objects with unfami…
SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning
Philip Schroeder, Thomas Weng, Karl Schmeckpeper +3
Vision-language models (VLMs) have shown impressive capabilities across diverse tasks, motivating efforts to leverage these models to supervise robot learning. However, when used a…
You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector
Omkar Patil, Ondrej Biza, Thomas Weng +9
Generative robot policies trained on demonstrations using behavior cloning often learn actions that are sub-optimal or misaligned with respect to the downstream task. Policy improv…
ROVER: Recursive Reasoning Over Videos with Vision-Language Models for Embodied Tasks
Philip Schroeder, Ondrej Biza, Thomas Weng +2
Vision-language models (VLMs) have exhibited impressive capabilities across diverse image understanding tasks, but still struggle in settings that require reasoning over extended s…
On-Robot Reinforcement Learning with Goal-Contrastive Rewards
Ondrej Biza, Thomas Weng, Lingfeng Sun +6
Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-r…
ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter
Yaoyao Qian, Xupeng Zhu, Ondrej Biza +5
Robotic grasping in cluttered environments remains a significant challenge due to occlusions and complex object arrangements. We have developed ThinkGrasp, a plug-and-play vision-l…
Equivariant Offline Reinforcement Learning
Arsh Tangri, Ondrej Biza, Dian Wang +3
Sample efficiency is critical when applying learning-based methods to robotic manipulation due to the high cost of collecting expert demonstrations and the challenges of on-robot p…
Imagination Policy: Using Generative Point Cloud Models for Learning Manipulation Policies
Haojie Huang, Karl Schmeckpeper, Dian Wang +6
Humans can imagine goal states during planning and perform actions to match those goals. In this work, we propose Imagination Policy, a novel multi-task key-frame policy network fo…
Equivariant Single View Pose Prediction Via Induced and Restricted Representations
Owen Howell, David Klee, Ondrej Biza +2
Learning about the three-dimensional world from two-dimensional images is a fundamental problem in computer vision. An ideal neural network architecture for such tasks would levera…
On Robot Grasp Learning Using Equivariant Models
Xupeng Zhu, Dian Wang, Guanang Su +3
Real-world grasp detection is challenging due to the stochasticity in grasp dynamics and the noise in hardware. Ideally, the system would adapt to the real world by training direct…
One-shot Imitation Learning via Interaction Warping
Ondrej Biza, Skye Thompson, Kishore Reddy Pagidi +7
Imitation learning of robot policies from few demonstrations is crucial in open-ended applications. We propose a new method, Interaction Warping, for learning SE(3) robotic manipul…
Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction
David M. Klee, Ondrej Biza, Robert Platt +1
Predicting the pose of objects from a single image is an important but difficult computer vision problem. Methods that predict a single point estimate do not predict the pose of ob…
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi +3
Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning. Recent slot-based neural networks that learn about obje…
Graph-Structured Policy Learning for Multi-Goal Manipulation Tasks
David Klee, Ondrej Biza, Robert Platt
Multi-goal policy learning for robotic manipulation is challenging. Prior successes have used state-based representations of the objects or provided demonstration data to facilitat…
Image to Icosahedral Projection for Object Reasoning from Single-View Images
David Klee, Ondrej Biza, Robert Platt +1
Reasoning about 3D objects based on 2D images is challenging due to variations in appearance caused by viewing the object from different orientations. Tasks such as object classifi…
Binding Actions to Objects in World Models
Ondrej Biza, Robert Platt, Jan-Willem van de Meent +2
We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms. We propose two attention mechanisms for binding actions to obj…
Learning Symmetric Embeddings for Equivariant World Models
Jung Yeon Park, Ondrej Biza, Linfeng Zhao +2
Incorporating symmetries can lead to highly data-efficient and generalizable models by defining equivalence classes of data samples related by transformations. However, characteriz…
Sample Efficient Grasp Learning Using Equivariant Models
Xupeng Zhu, Dian Wang, Ondrej Biza +3
In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses in . In this paper, we recognize that the opt…
Factored World Models for Zero-Shot Generalization in Robotic Manipulation
Ondrej Biza, Thomas Kipf, David Klee +3
World models for environments with many objects face a combinatorial explosion of states: as the number of objects increases, the number of possible arrangements grows exponentiall…
The Impact of Negative Sampling on Contrastive Structured World Models
Ondrej Biza, Elise van der Pol, Thomas Kipf
World models trained by contrastive learning are a compelling alternative to autoencoder-based world models, which learn by reconstructing pixel states. In this paper, we describe…
Action Priors for Large Action Spaces in Robotics
Ondrej Biza, Dian Wang, Robert Platt +2
In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or curriculum learning. As a conseque…
Learning Discrete State Abstractions With Deep Variational Inference
Ondrej Biza, Robert Platt, Jan-Willem van de Meent +1
Abstraction is crucial for effective sequential decision making in domains with large state spaces. In this work, we propose an information bottleneck method for learning approxima…
Online Abstraction with MDP Homomorphisms for Deep Learning
Ondrej Biza, Robert Platt
Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning…