Industry Briefing
A single destination for timely, editor-curated robotics news from around the world.
Visual Language Models Train Robots to Read Human Emotions
A recent study led by Seung Chan Hong at the University of Melbourne explores the emotional capabilities of collaborative robots as they increasingly work alongside humans. Published on May 18 in IEEE Robotics and Automation Letters, the research investigates how robots can better understand human emotions through contextual cues, beyond just facial expressions. Involving 40 volunteers, the study trained a vision language model (VLM) to interpret emotions based on video interactions where robots handed objects to humans. The VLM outperformed traditional AI systems, scoring 0.86 in emotional accuracy compared to 0.77 for conventional methods. This improvement is attributed to the VLM's ability to consider the entire context of interactions rather than isolated facial expressions. In a follow-up experiment, participants interacted with a robot that was programmed to make an error, receiving either an emotionally adaptive apology or a standard one. The majority preferred the adaptive response, but trust in the robot diminished after it failed to complete its task, highlighting that emotional responses cannot compensate for a lack of functionality. While the VLM effectively recognized emotions from a third-party perspective, its accuracy dropped when compared to participants' self-reported feelings, indicating that robots still struggle to fully understand human emotions. The findings suggest that while emotional adaptivity is valuable, the primary concern for users remains the robot's competence in performing tasks.
Robotics Journal-watch Ai-models Emotion-recognition
Māori Text-to-Speech Model Spurns Big Tech’s Values
Researchers at the University of Waikato in New Zealand have developed a high-fidelity synthetic voice for te reo Māori, the indigenous language of the country, in response to concerns over the ownership and control of Māori language data by foreign technology companies. Led by associate professor Te Taka Keegan and his former master's student Kingsley Eng, the project was motivated by a desire for "sovereign digital systems" that prioritize Māori ownership of their language resources. The initiative began with the recording of 4.5 hours of data from Ngaringi Katipa, a fluent speaker and language mentor, which was later expanded to 7 hours and 45 minutes. The researchers faced challenges due to the unique linguistic features of te reo Māori, such as vowel length and digraphs, which can alter meanings. They employed a phoneme-based approach to training the text-to-speech model, utilizing open-source tools and testing various neural architectures to achieve an effective AI voice with a word error rate of 6.78 percent. Despite receiving funding from Google, Keegan emphasized that the ownership of the voice model remains a collective responsibility of the Māori community, particularly the tribes affiliated with Katipa. The project aims to empower Māori language speakers and establish a framework for similar initiatives among other indigenous communities globally. Keegan envisions a future where community-owned language models can preserve and promote indigenous knowledge, ensuring that technology serves to empower rather than diminish cultural heritage.
Artificial-intelligence Languages Ai-models
DAIMON Robotics Wants to Give Robot Hands a Sense of Touch
In April 2023, DAIMON Robotics, a Hong Kong-based company, launched Daimon-Infinity, touted as the world's largest omni-modal robotic dataset for physical AI. This extensive dataset, which includes high-resolution tactile sensing data from over 80 real-world scenarios and 2,000 human skills, aims to enhance robot manipulation capabilities across various tasks, from household chores to industrial assembly lines. The initiative is backed by collaborations with prominent partners, including Google DeepMind, Northwestern University, and the National University of Singapore. Prof. Michael Yu Wang, co-founder and chief scientist of DAIMON, emphasized the importance of tactile feedback in improving robotic dexterity, advocating for a shift from the traditional Vision-Language-Action (VLA) model to a more integrated Vision-Tactile-Language-Action (VTLA) framework. This transition is crucial for enabling robots to perform complex manipulation tasks effectively, especially in environments where visual data alone is insufficient. Recognizing a significant data gap in the robotics industry, DAIMON has committed to open-sourcing 10,000 hours of its dataset to support broader research and development efforts. The company aims to accelerate the deployment of embodied AI by providing high-quality tactile data, which is essential for training robots to interact with their surroundings more naturally and effectively. As the robotics landscape evolves, DAIMON's innovative approach positions it as a key player in advancing the capabilities of humanoid robots in real-world applications.
Type-sponsored Factory-robots Tactile-sensing Ai-models Embodied-intelligence
Better Hardware Could Turn Zeros into AI Heroes
Researchers at Stanford University have developed a groundbreaking hardware accelerator named Onyx, designed to enhance the efficiency of artificial intelligence (AI) computations by leveraging the concept of sparsity. This innovation comes in response to the growing energy demands and carbon footprint associated with increasingly large language models (LLMs), such as Meta's recent Llama release, which boasts 2 trillion parameters. Onyx aims to address the limitations of current hardware, which often fails to fully utilize the sparse nature of AI models, where many parameters are effectively zero. By re-engineering the architecture to support both sparse and dense computations, Onyx achieves significant energy savings—consuming up to one-seventieth the energy of traditional CPUs and performing computations eight times faster on average. The development of Onyx reflects a broader trend in AI research, where experts are exploring new algorithms and hardware solutions to mitigate the environmental impact of AI technologies. The team at Stanford plans to expand Onyx's capabilities to support a wider range of AI operations, potentially revolutionizing the field and paving the way for more sustainable AI practices. As the demand for efficient AI solutions grows, Onyx represents a promising step toward balancing performance and energy consumption in machine learning.
Ai-models Gpus Energy-efficiency Data-compression
