11 accepted papers
Timely detection of incipient slip is critical for delicate robotic grasping and dexterous manipulation. However, existing learning-based methods suffer from detection latency and high computational demands. In this paper, we present an ultra-fast lightweight incipient slip detection framework based
Timely detection of incipient slip is critical for delicate robotic grasping and dexterous manipulation. However, existing learning-based methods suffer from detection latency and high computational demands. In this paper, we present an ultra-fast lightweight incipient slip detection framework based…
While music remains a challenging domain for generative models like Transformers, recent progress has been made by exploiting suitable musically-informed priors. One technique to leverage information about musical structure in Transformers is inserting such knowledge into the positional encoding (PE…
In recent years, foundation models have significantly advanced data-driven systems across various domains. Yet, their underlying properties, especially when functioning as feature extractors, remain under-explored. In this paper, we investigate the sensitivity to audio effects of audio embeddings ex…
In this letter, we present a novel portable strategy for the autonomous exploration of highly unstructured three-dimensional environments using ground robots. The proposed planner leverages elevation mapping to estimate traversability, enabling efficient environment mapping while conserving computat
Sequential decision-making can be formulated as a conditional generation process, with targets for alignment with human intents and versatility across various tasks. Previous return-conditioned diffusion models manifest comparable performance but rely on well-defined reward functions, which requires…
Music generated by deep learning methods often suffers from a lack of coherence and long-term organization. Yet, multi-scale hierarchical structure is a distinctive feature of music signals. To leverage this information, we propose a structure-informed positional encoding framework for music generat…
Deep convolutional networks (convnets) in the time–frequency domain can learn an accurate and fine-grained categorization of sounds. For example, in the context of music signal analysis, this categorization may correspond to a taxonomy of playing techniques: vibrato, tremolo, trill, and so forth. Ho…
In this paper, we extend a famous motion planning approach, GPMP2, to multi-robot cases, yielding a novel centralized trajectory generation method for the multi-robot formation. A sparse Gaussian Process model is employed to represent the continuous-time trajectories of all robots as a limited numbe…
Impact mitigation is crucial to the stable locomotion of legged robots, especially in high-speed dynamic locomotion. This paper presents a leg locomotion system, including the nonlinear active compliance control and the active impedance control for the steel wire transmission-based legged robot. The…
Playing techniques are important expressive elements in music signals. In this paper, we propose a recognition system based on the joint time-frequency scattering transform (jTFST) for pitch evolution-based playing techniques (PETs), a group of playing techniques with monotonic pitch changes over ti…