5 papers
Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition
Benedict Quartey, Sebastian Castro, Eric Rosen +3
Learning from Demonstration (LfD) enables robots to learn complex behaviors from expert examples, yet existing approaches often fail to generalize to new compositions of known skil…
Language Model as Planner and Formalizer under Constraints
Cassie Huang, Stuti Mohan, Ziyi Yang +2
LLMs have been widely used in planning, either as planners to generate action sequences end-to-end, or as formalizers to represent the planning domain and problem in a formal langu…
LEGS-POMDP: Language and Gesture-Guided Object Search in Partially Observable Environments
Ivy Xiao He, Stefanie Tellex, Jason Xinyu Liu
To assist humans in open-world environments, robots must interpret ambiguous instructions to locate desired objects. Foundation model-based approaches excel at multimodal grounding…
Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills
Benned Hedegaard, Yichen Wei, Ahmed Jaafar +4
Task and motion planning (TAMP) is a well-established approach for solving long-horizon robot planning problems. Although TAMP methods have historically assumed that each task-leve…
Verifiably Following Complex Robot Instructions with Foundation Models
Benedict Quartey, Eric Rosen, Stefanie Tellex +1
When instructing robots, users want to flexibly express constraints, refer to arbitrary landmarks, and verify robot behavior, while robots must disambiguate instructions into speci…