Alfie
The Alfie is a Industrial humanoid robot developed by Prosper Robotics. It features 170 cm tall, 100 kg. The Alfie is priced at $13K, in development.
Primary use case: Serving people
Height
170 cm
Weight
100 kg
Price
$13K
Specifications
Physical
Height
170 cm
Weight
100 kg
Locomotion
Bipedal
Manipulation
Hand Type
unknown
Power & Autonomy
Autonomy Level
unknown
AI & Compute
AI Capabilities
Hybrid teleoperation and AI autonomy; remote human operators via VR headsets guide the robot while generating training data for autonomous learning; progressive shift from teleoperation to AI-driven control over time
Commercial
Price
$13K
Availability
in development
Primary Use Case
Serving people
Related news
Hello Panasonic, Telsa cuts Optimus' memory budget, and Munich takes center stage
RobCo reaches $1 billion valuation as investors back US expansion
RobCo has surpassed a $1 billion valuation, doubling in just nine months, as it expands in the US and prepares to commercially launch its autonomous industrial robot Alfie in March 2027. The round included new investment from Cherry Ventures and European Tech Collective, with existing backers Sequoia, Lightspeed, Greenfield, Kindred, Lingotto, and Promus Ventures also participating. CEO Roman Hölzl has relocated to the US to drive expansion, with customer operations spanning more than a dozen states and manufacturing in Austin, Texas.
German robotics startup RobCo hits $1 billion valuation
RobCo surpassed a $1 billion valuation through a $40 million employee secondary share sale, with most of the proceeds going to selling employees rather than the company. The transaction doubled the approximately $500 million valuation reached after a $100 million fundraising round in January and included new investors Cherry Ventures and European Tech Collective alongside existing backers. RobCo has sold more than 1,000 robots and plans to commercially launch its AI-enabled, two-armed Alfie robot on March 4, 2027.
Learning to Tune a Mobile Robot Planner: Hierarchical Architecture and Sim‐to‐Real Transfer
ABSTRACT Autonomous navigation in complex, unstructured environments poses a significant challenge, with traditional planners lacking adaptability and end-to-end learning methods hindered by data dependency or training instability. Learning-based hybrid methods that automatically tune traditional planner parameters are a promising compromise, but we identify an underlying architectural limitation that contributes to constraining their performance: a low-frequency, high-latency control loop. This paper resolves this architectural bottleneck by proposing a multi-rate, hybrid hierarc