4 papers · 1 filter
SIR: Structured Image Representations for Explainable Robot Learning
Paul Mattes, Jan Schwab, Jens Bosch +5
Existing robot policies based on learned visual embeddings lack explicit structure and are sensitive to visual distractions. Thus, the representations that drive their behaviour ar…
SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale
Nils Blank, Paul Mattes, Maximilian Xiling Li +5
This work introduces Spatial Annotations from Robot Demonstrations with Reliability Calibration (SPARC), a risk-aware framework that automatically labels robot demonstrations with…
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning
Hongyi Zhou, Weiran Liao, Xi Huang +11
We present the B-spline Encoded Action Sequence Tokenizer (BEAST), a novel action tokenizer that encodes action sequences into compact discrete or continuous tokens using B-splines…
Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models
Nils Blank, Moritz Reuss, Marcel Rühle +4
A central challenge towards developing robots that can relate human language to their perception and actions is the scarcity of natural language annotations in diverse robot datase…