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Pieter Abbeel
Robert Anderson
Gopala Anumanchipalli
Peter Bartlett
Alexandre Bayen
Christian Borgs
John Canny
Jennifer Chayes
Trevor Darrell
Anca Dragan
Laurent El Ghaoui
Ronald Fearing
Sanjam Garg
Ken Goldberg
Joseph Gonzalez
Alison Gopnik
Nika Haghtalab
Bjoern Hartmann
Jiantao Jiao
Michael Jordan
Angjoo Kanazawa
Kurt Keutzer
Dan Klein
Sergey Levine
Can Liu
Yi Ma
Michael Mahoney
Jitendra Malik
Kristofer Pister
Raluca Ada Popa
Kannan Ramchandran
Stuart Russell
Anant Sahai
Alberto Sangiovanni-Vincentelli
Sanjit Seshia
Dawn Song
Koushil Sreenath
Ion Stoica
Claire Tomlin
David Wagner
Martin Wainwright
Stella Yu
Avideh Zakhor
Results
Active Visual Planning: Handling Uncertainty in Perception, Prediction, and Planning Pipelines
Adaptive Long-Distance Navigation for Autonomous Drones
Adding Safety and Robustness to Learning for Robots by Learning on Robots
Addressing Challenges in Large-scale Distributed AI Systems
Alpa: A Distributed System for Training and Serving Large Models
Amazon-Berkeley Objects: A Large-Scale Dataset for 3D Object Understanding
Animating Animals from Video
Ashera: Neural Optimization Modulo Theories
Automated Collision Prediction in Autonomous Systems with Monocular Camera
Automated State and Action Space Design for Multi-Objective Reinforcement Learning
Automating Multi-Agent Curriculum Learning with Probabilistic Programs
Coherent and Consistent Long Story Generation
Combating Hallucination in Conditional Sequence Generation
Compressing High Capacity Models with Implicit Neural Networks and Frank-Wolfe
Control of Microrobots with Data and Computationally Efficient Reinforcement Learning
Data Curation for Web-Scale Datasets
Designing Pro-social Networks through Multi-Agent Learning Theory
Dimension-free Statistical and Computational Guarantee for Optimal Transport
Distributed Learning: Privacy and Data Summarization
Dynamic Compression Techniques for Efficient Transformers
Emergent Collaboration for Heterogeneous Multi-Robot Rearrangement
Enabling Non-Experts to Annotate Complex Logical Forms at Scale
Experimenting with Zero-Knowledge Proofs of Training
Factorized language representations with knowledge and logic
Fate of Snow
Formal Skill Representation and Composition for Task Generalization
Generalizing Domain Randomization for Zero-Shot Transfer
Grounded and Modular Vision and Language Learning
Grounded and Structured Self-Supervised Pre-training of Speech for Spoken Language Model
GuBERT: Grounded units for Self-Supervised Pre-training of Speech
Hardware Software Co-Design for NLP and Recommendation Systems
Hierarchical Model-Based Reinforcement Learning with Temporal Abstractions
Interactive Learning from Vision and Touch
Investigations into the Complexity of Nonconvex Optimization
Large-scale 3D Reconstruction from Multi-view Image Datasets
Learning Dexterous In-Hand Manipulation with Vision and Touch
Learning From Play in Children and Robots: Who Can Train a Robot Better?
Learning human-robot collaboration from human feedback
Learning Large Touch-Vision-Language Models Using Self-Supervised Robot Learning
Learning Safety-Assured Collaborative Quadrupedal Manipulation
Learning Selective Invariance Upon Parametric Density Functions of Transformations
Learning Space Partitions for Path Planning
Learning Successor Affordances as Temporal Abstractions
Learning to Collaborate with Human Players
Learning-based Safe Navigation for Dynamic Legged Robots
Learning-Driven Exploration For Search
Leveraging Demonstrations with Goal-Directed Multi-Task Bisimulation
Local Parametric Learning Rules for Parallel and Scalable Training
Long Term Video Understanding
Long-Range Understanding and Consistency in Story Generation
Low-Data Learning for Assistive Video Description
ML-Based Robotic Manipulation via the Use of Diverse Datasets
Modeling Interpersonal Multimodal Signals in Social Conversation
Modeling latent variable for self-supervised learning
Multi-agent Social Learning
Multiscale Modeling for Control
Never Decrypt Data Lake
NumS: NumPy API-Compatible Framework backed by Ray
Offline Recovery RL: Offline Reinforcement Learning with Safe Online Adaptation
Optimal Data Augmentation Strategy Search
Personalized federated learning: new algorithms and statistical rates,
Pre-trained Representations for Language-Guided Web Navigation
Realistic Large-Scale Benchmark for Adversarial Patch
Regret Bounds for Contextual Bandits Under Slate Feedback
Reinforcement Learning in High Dimensional Systems
Robust Image Classification via Parts and Disentangled Attributes
Robustness for Deep Learning/Ethical AI Through Human Value Modeling
Safe and Sound: Learning Locomotion Skills Across Robot Morphology
Safe Robotic Learning Via Reachability Theory
Scene Sketch to Photo Synthesis
Secure and Privacy-Preserving Federated Learning
Self Supervised Semantic Segmentation in the Wild
Self-supervised Learning for Generic Visual Representation
Self-supervised Open-World Segmentation
Statistically Efficient Offline RL with General Function Approximation
Task-Specific World Models for Robotic Manipulation
Towards a Unified Understanding of Privacy and Generalization for Better Algorithm Design
Towards Human-like Attention
Towards Learning and Auditing Private Foundation Models
Towards Robust Neural Networks with Conditional Generative Models
Training Sparse High Capacity Models with Implicit Neural Networks and Frank-Wolfe
Uncertainty Aware Machine Learning for Model Based Planning and Control
Universal Representation Learning for Control
Unsupervised Environment Design for Multi-task Reinforcement Learning
Using Deep Reinforcement Learning to Generalize Search in Games
Video Representation Learning for Global and Local Features
Weakly Supervised Multimodal Feature Representation Learning from Video
Writing with Speech — Using LLMs for Gist-Level Manipulation of Spoken Text
X-Ray for Lateral Access Mechanical Search