MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction

Prasad, V.; Koert, Dorothea; Stock‐Homburg, Ruth; Peters, Jan; Chalvatzaki, Georgia

doi:10.1109/humanoids53995.2022.10000239

Cited by 4 publications

(1 citation statement)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since many interactive tasks can naturally be broken down into underlying segments or phases that are then sequenced to achieve suitable behavior, previous works have explored learning HRI from demonstrations using Gaussian Mixture Models (GMMs) or, additionally, Hidden Markov Models (HMMs) with an underlying Mixture of Gaussians structure [3,4,13,14,11,10,15]. However, Hidden Markov Models have limitations in representing transition states.…”

Section: Introductionmentioning

confidence: 99%

Transition State Clustering for Interaction Segmentation and Learning

Hahne,

Prasad,

Kshirsagar

et al. 2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

Self Cite

View full text Add to dashboard Cite

Hidden Markov Models with an underlying Mixture of Gaussian structure have proven effective in learning Human-Robot Interactions from demonstrations for various interactive tasks via Gaussian Mixture Regression. However, a mismatch occurs when segmenting the interaction using only the observed state of the human compared to the joint state of the human and the robot. To enhance this underlying segmentation and subsequently the predictive abilities of such Gaussian Mixture-based approaches, we take a hierarchical approach by learning an additional mixture distribution on the states at the transition boundary. This helps prevent misclassifications that usually occur in such states. We find that our framework improves the performance of the underlying Gaussian Mixturebased approach, which we evaluate on various interactive tasks such as handshaking and fistbumps. CCS CONCEPTS• Theory of computation → Unsupervised learning and clustering; • Computing methodologies → Mixture modeling; Learning from demonstrations.

show abstract