ECHO-G is a framework for generating full‑body co‑speech motion for humanoid robots, jointly conditioned on speech audio and timed transcripts. Its Speech‑Grounded Diffusion Transformer (SGDiT) fuses frame‑aligned acoustic features with token‑level linguistic context, preserving distinct granularities while modeling one‑to‑many utterance‑motion relationships directly in robot space. The authors introduce a BEAT2‑derived audio‑text‑robot dataset, a benchmark for co‑speech characteristics, robot‑motion quality, and runtime efficiency, and demonstrate that direct robot‑space generation outperforms human‑motion generation and retargeting pipelines, with joint audio‑text conditioning yielding superior results in both quantitative evaluation and a video‑rating study.
"whyItMatters":"The study provides a new dataset, benchmark, and a demonstrably effective method for generating realistic co‑speech motion directly in robot space, advancing practical humanoid robot interaction."
By Yizhao Li, Pusen Gao, Ming Wang, Shaojie Shen, Shuo Yang, Hao Xu
Generating full-body co-speech motion for humanoid robots requires coordinating speech prosody, linguistic content, and embodiment-specific motion. To this end, we present ECHO-G, a framework jointly...
arXiv:2510. 01711v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained Vision-Language Models (VLMs).
By Taeyoung Kim, Jimin Lee, Myungkyu Koo, Dongyoung Kim, Kyungmin Lee, Changyeon Kim, Younggyo Seo, Jinwoo Shin
The paper introduces the Neural Action Codec (NAC), a convolutional encoder‑decoder architecture that treats short robot action trajectories as multi‑channel 1D signals and compresses them using a multi‑scale residual vector quantization (RVQGAN) model. NAC replaces traditional discrete action tokenizers with a compact, ordered token space via offset codebooks, allowing standard autoregressive policies to operate over short, structured sequences while a Vocos‑style decoder reconstructs the actions. Experiments on LIBERO‑10, RoboMimic, and real‑world manipulation tasks show that NAC achieves higher reconstruction fidelity and better average success rates than existing binning, FAST, and VQ‑based tokenizers at comparable or improved compression rates.
By Ahad Jawaid, Yu Xiang
arXiv:2607. 07294v1 Announce Type: cross Abstract: Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot must anticipate conversational dynamics rather than merely react to pauses.
By Antonio Cano, Guillermo P\'erez, Luis Merino, Randy Gomez
arXiv:2507. 05116v5 Announce Type: replace-cross Abstract: Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language.
By Juyi Lin, Amir Taherin, Arash Akbari, Arman Akbari, Lei Lu, Guangyu Chen, Taskin Padir, Xiaomeng Yang, Weiwei Chen, Yiqian Li, Xue Lin, David Kaeli, Pu Zhao, Yanzhi Wang
The paper introduces Jarvis, an offline, edge‑deployable voice assistant designed for autonomous racecars. It combines speech recognition, synthesis, and a lightweight text‑to‑command classifier fine‑tuned from the Mistral 7B model to provide high‑level behavioral commands. Experiments show 97.63 % intent recognition accuracy with an average latency of 1.39 s, outperforming larger online‑hosted models and enabling quick response times for time‑critical driving tasks.
By Daniel Henel, Frederik Werner, Alexander Langmann, Johannes Betz
Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot must anticipate conversational dynamics rather than merely react to pauses. This work presents a Multimodal Voice Activity Projection (MM-VAP) framework that extends the original audio-only VAP formulation to synchronized audio-visual inputs while preserving its self-supervised future-projection objective.
arXiv:2603.15046v2 Announce Type: replace-cross
Abstract: In this study, we address the problem of language-guided robotic manipulation, where a robot is required to manipulate a wide range of object...
By Yusuke Takagi, Motonari Kambara, Daichi Yashima, Koki Seno, Kento Tokura, Komei Sugiura
arXiv:2606. 16731v1 Announce Type: cross Abstract: Current multiparty turn-taking models often rely on complex microphone arrays or multi-camera setups, limiting their applicability in human-robot interaction scenarios.
By Haotian Qi, Gabriel Skantze
NAVIR is an end‑to‑end audio‑visual speech recognition system designed for the BrainChip Akida neuromorphic processor, which only supports sequential 2‑D convolutions. The architecture separates spatial and temporal encoding into three AkidaNet modules—per‑frame visual, temporal video, and spectrogram audio encoders—fused by a lightweight predictor and decoded with constrained beam search. Trained with CTC on noise‑augmented audio and fine‑tuned via quantization‑aware training, the quantized model achieves 14.0% WER on GRID’s unseen‑speaker split and 3.3% on overlapped‑speaker split, outperforming audio‑only baselines, and delivers 98.6% command accuracy at 1.5% WER on an industrial‑command corpus, while offering a 13‑fold energy advantage over conventional ANNs and roughly 5‑fold lower energy per inference than a Raspberry Pi CPU.
By Leonidas Delimpasis, Panagiota Moraiti, Antonis Porichis, Panos Chatzakos, Michail Karamousadakis
Robots operating in real-world environments must execute complex, multi-step bimanual tasks over long horizons rather than single, isolated actions. Current manipulation datasets struggle to support t...