🔬 University#107 Austin, United States
Robot Perception and Learning Lab
The Robot Perception and Learning Lab at UT Austin investigates the synergistic relations of perception and action in embodied agents. The lab develops algorithms and systems for general-purpose robot autonomy, enabling robots to reason about the world through sensing and learn new tasks adaptively.
perceptionlearningembodied AIautonomous systemsmanipulation
Recent publications
All papers →Matched by this lab's specialties (keyword overlap + direct affiliation)
MANIPULATION
📊 0 cites
A hierarchical approach to imitation learning for manipulation tasks requiring time varying forces
Rishabh Shukla, Adithya Santhosh, Shaili Gandhi +2 more
Robotics and Computer-Integrated Manufacturing · 2026
MANIPULATION
Open access
How VLAs Fail Differently: Black-Box Action Monitoring Reveals Architecture-Specific Failure Signatures
Krishnam Gupta
2026
MANIPULATION
Open access
What Frozen VLAs Already Know About Success: A Probing Study of Value-Like Structure in Foundation Robot Policies
Jiachen Zhang, Junnan Nie, Junyi Lao +4 more
2026
PERCEPTION
Open access
What-If World: A Causal Benchmark for General World Models in Embodied Scenarios
Kunlin Cai, Rui Song, Jinghuai Zhang +7 more
2026
PERCEPTION
Open access
OSMa-Bench++: Toward Open-Ended Benchmarking of Semantic Mapping for Manipulation with Prompt-Generated Synthetic Scenes
Regina Kurkova, Maxim Popov, Sergey Kolyubin
2026
MANIPULATION
Open access
EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models
Perry Dong, Kuo-Han Hung, Tian Gao +2 more
2026