Finance Lead - FP&A, IR

Ricoh has invested in Telexistence through its RICOH Innovation Fund, forming a capital alliance to develop AI-powered robotic automation for retail and logistics. Telexistence operates TX GHOST and TX SCARA robots that automate beverage restocking in convenience stores via a Robot-as-a-Service subscription model, already deployed by major Japanese retailers. The partnership combines Ricoh's digital services and AI expertise with Telexistence's robotics platform and remote operation technology. Telexistence uses a hybrid approach merging AI with human-assisted remote operation, enabling robots to function in dynamic retail environments whilst collecting data to improve AI models. Ricoh and Ricoh Japan will jointly explore research, development, and commercialisation opportunities in Physical AI, targeting intelligent automation solutions that address labour shortages and rising workforce costs.

Telexistence announces Physical AI Fellowship 2026 results. June 10, 2026 Unveiling Results of VLA-Based Single-Policy Autonomous Control and Joint Implementation with NVIDIA World Model "DreamZero" Telexistence Inc. (TX) today unveiled the results of its participation in the Physical AI Fellowship 2026, a global program powered by Amazon Web Services(AWS), NVIDIA and MassRobotics that helps high-potential robotics stratups build, refine, and scale physical AI solutions. TX is releasing a VLA-based autonomous manipulation demo on its humanoid robot, alongside the results of joint implementation with NVIDIA's World Model "DreamZero." Under the theme "From Real Humanoid Data to Physical AI," TX is publicly demonstrating for the first time an end-to-end pipeline that feeds large-scale, real-world teleoperation data directly into foundation model training - closing the loop from physical experience to Physical AI. 1. A single-policy model autonomously bags convenience store items. The core achievement of this demo is not a sequence of chained subtasks - it is a single VLA model (single policy) processing perception to action end-to-end. One model. One pass. No handoffs between sub-modules. The demo footage shows TX's humanoid robot at a Japanese convenience store checkout counter, grasping multiple items - PET bottles, onigiri, snacks - with both arms and placing them into a shopping bag. The "Autonomous" label onscreen confirms zero teleoperation intervention throughout. The single-policy approach is a deliberate architectural bet for scaling Physical AI. TX is going all-in on this direction. 2. Joint Implementation with NVIDIA "DreamZero" - bridging real data and model learning. TX is also releasing results from its joint implementation work with NVIDIA's World Model "DreamZero." Through the Physical AI Fellowship, TX has been working alongside the NVIDIA and AWS teams to implement DreamZero with a focus on real-world applicability. DreamZero is a World Model that predicts future states of a robot and its surrounding scene. In addition to predicting behavior in simulated environments, TX has demonstrated offline prediction from real humanoid teleoperation data - validating that data collected in the physical world can be fed directly into foundation model training. A World Model with grounded understanding of real-world dynamics has the potential to enable Physical AI systems to handle novel situations more robustly. TX continues this work in close collaboration with NVIDIA and AWS teams, positioning it as central to its robotics foundation model development. 3. Where Tx Inc is - and what Tx Inc is solving next. TX is releasing this demo not as a finished product, but as an honest snapshot of where the work stands today. The current technical challenges are clear - and they define TX's next development agenda. Autonomous operation has not yet reached practical deployment speed. The demo footage includes both 1x and 8x playback - a transparent acknowledgment that speed is the next major hurdle. Translating slow, human-demonstrated teaching data into fast, robot-optimized motion is the top priority on the technical roadmap. Motion Smoothness Current motion output still exhibits jerkiness. TX is actively working on how to best convert VLA model outputs into final robot control commands, as well as optimizing action representation in the VLA model itself and its training dataset. Generalization A shift of just a few centimeters in object placement can cause a drop in task success. TX believes this is fundamentally a data volume and diversity problem - solvable through scale. The open question of exactly how much data is needed is itself an important development challenge. TX's end-to-end ownership of hardware design, manufacturing, and real-world robot operation gives it a unique edge in pursuing the quantity and diversity of data needed to close this gap. Joint implementation with NVIDIA on DreamZero is also a key lever for improving data efficiency. 4. Why join TX now. Every challenge described above is an unsolved problem that the world's top institutions and companies are actively working on. TX is tackling them head-on with a unique set of advantages: * Real-world scale: TX already operates robots in live commercial convenience store environments - accumulating real-world data that academic labs simply cannot access. * Full-stack ownership: Hardware, data collection, foundation models, and control all under one roof - meaning implementation results go to real robots immediately. * Infrastructure: Large-scale training on state-of-the-art GPU clusters including AWS, simulation infrastructure via IsaacSim in collaboration with the NVIDIA team, and direct access to AWS Generative AI Innovation Center engineers. * Tight feedback loop: World model development through DreamZero and real-robot deployment run in the same team. Tx Inc is looking for engineers who want to work somewhere that's building the answers - not just applying them. Open positions. * Head of Robotics System Engineering, Humanoid Team * VP of Robotic Foundation Model * Logistics Robotics Hardware Engineer - Next Gen Products * Product Manager - Retail System * Engineering Manager - Store Applications "This demo is a starting point, not a finish line. Getting a single-policy VLA working on real hardware is a milestone - but the walls of speed, smoothness, and generalization are right in front of us. I want to work on these questions alongside people who are ready to face them head-on." - Genki Sano, Co-Founder & CTO, Telexistence About Physical AI Fellowship. The Physical AI Fellowship is a global program co-led by AWS, NVIDIA, and MassRobotics, designed to help high-potential robotics startups mature promising prototypes into real-world deployments. The 2026 cohort selected only 9 companies worldwide. TX is the first Japanese company to be selected. What fellows receive: * $200,000 in AWS credits * Embedded support from AWS Generative AI Innovation Center scientists and engineers * Access to NVIDIA Isaac and Cosmos frameworks (via NVIDIA Inception program) * Ecosystem support and facility access through MassRobotics * Demo opportunities at major events including AWS re:Invent 2026 About Telexistence. Telexistence Inc. (TX) is an AI robotics company on a mission to "grasp every single object in the world with our own hands." TX develops a full-stack platform integrating robotics foundation models, World Models, advanced manipulation AI, and purpose-built hardware - deploying intelligent robots across retail, logistics, and manufacturing. Press inquiries: Email: [email protected]