
Why robots that can’t communicate naturally won’t be adopted
For humanoid robots to operate in the real world, they’ll need more than a sense of sight. | Source: Adobe Stock At recent major tech events, humanoid robots were everywhere. Some of them walked, navigated, and manipulated objects with a level of dexterity that would have seemed unrealistic just a few years ago. And yet, despite all of this progress, I find myself consistently underwhelmed when I try to communicate with them at Treble. Supporting the development of advanced audio and voice
Humanoid robots have made impressive progress in movement and vision, but they lack natural communication abilities through voice and audio perception. For robots to be widely adopted, they need to operate effectively in real-world environments that are noisy and chaotic, yet current audio and voice systems in robotics remain underdeveloped compared to their visual capabilities. The focus on vision over hearing reflects practical constraints in how AI systems are trained, using primarily silent, visual environments rather than incorporating sound. Because humans evaluate robots not just on technical capability but on how it feels to interact with them, poor communication abilities create frustration and erode trust in ways that imperfect movement does not.
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Browse AI courses on MavenGoogle DeepMind releases new advanced robotics AI model and next-generation gene prediction model
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Can AI agents program industrial robots without training data? Berkeley says yes
The new RoboSimStudio is an open-source project that enables agentic AI coding of robotic simulation environments. | Credit: UC Berkeley UC Berkeley and NVIDIA have released open-source tools for a new approach: enabling teams of AI coding agents to write, simulate, and refine readable robot programs. Since the early days of industrial robots, programming, tuning, and troubleshooting robotic workcells has taken weeks to months. New research coming out of UC Berkeley has demonstrated that new age

Boston Dynamics adds wheels to Atlas humanoid to test whole-body control
Bridging the divide between deterministic physics-based control and policy-driven adaptation, Boston Dynamics has deployed a hybrid locomotion architecture on a wheeled variant of its Atlas humanoid. The “experiment” combined classical model-based motor control with whole-body reinforcement learning to maintain dynamic stability across unstructured, real-world terrain. In the video, Andy Wu, controls engineer for Atlas at Boston Dynamics, highlights some recent experiments that he

Practice Makes Precise: At CoRL, NVIDIA and Partners Reveal How Robots Rehearse Delicate Work in Simulation
Simulation, which has long been the proving ground for robots learning to walk, roll and hop, is now helping researchers teach robots tasks that require a finer touch: aligning gears, connecting cables and assembling small components. The shift from locomotion to manipulation is taking center stage at this week’s Conference on Robot Learning (CoRL) in Austin, Texas, alongside the rise of agentic robotics — a new paradigm that integrates frontier agents to create interpretable code for physical

Memories.ai builds standardized egocentric video pipeline
Memories.ai models label every clip: Memories-H0 for hand pose, EgoAction for action segments. | Credit: Memories.ai Memories.ai is building a data pipeline for robotics companies that combines standardized video collection with AI-powered annotation to turn everyday egocentric footage into training data for robots. The San Francisco-based company is developing software and AI to help robots understand the physical world. It builds video-understanding models and data infrastructure, and it colle