AthenaZero is a bimanual manipulator designed to minimize inertia without compromising control authority. By utilizing quasi-direct drive actuation and transmission remotization techniques, the system achieves an effective endpoint mass comparable to that of a human---about an order of magnitude less than conventional robot manipulators. This characteristic, combined with its inherent torque transparency, makes AthenaZero exceptionally well-suited for dynamic manipulation. We describe the methodology} that led to this design and demonstrate the robot's capabilities on three baseball-inspired tasks: throwing, catching, and batting, which showcase complex interactions on human-comparable timescales where milliseconds matter. AthenaZero was capable of throwing at speeds in excess of 30 m/s, with catching and batting at speeds in excess of 14 m/s over a short 7.3 m distance. Batting practice and a game of catch were subsequently performed in robot-to-robot and human-to-robot variations, showcasing the efficacy and adaptability of our system in tasks that require high acceleration.
The Will Smith 2004 blockbuster I, Robot predicts pervasive humanoid robots in 2035; investors agree, roboticists disagree.
Humanoid robots stand at the forefront of robotics, aiming to capture the agility, robustness, and expressivity of human movement in an anthropomorphic form. The locomotion control of humanoids has evolved from classical model-based methods to reinforcement learning powered by large-scale simulation and now to generative models that produce adaptive, whole-body behaviors, propelling humanoids toward operation in real-world environments. This survey positions humanoid control at a turning point, converging toward a unified paradigm of physics-guided generative intelligence that integrates optimization, learning, and predictive reasoning. We identify three core principles linking these paradigms: physics-based modeling, constrained decision-making, and adaptation to uncertainty. Building on these connections, we provide recommendations for researchers and outline open challenges in safety, accessibility, and human-level capability. These directions represent a transformation from engineered stability to intelligent autonomy, laying the groundwork for humanoid generalists capable of operating safely, collaborating naturally, and extending human capability in the open world.
We are entering an era where AI enables multipurpose applications, but the mechanical hardware must be done right.
The human brain can process spatial action through either egocentric (self-centered) or allocentric (environment-centered) perspectives. Despite this cognitive ability, teleoperation of robotic systems has been predominantly egocentric, wherein operators control a single robot from an inside-out perspective with limited situational overview. This paper proposes a scalable paradigm for simultaneously teleoperating multiple robotic systems from an allocentric perspective that enables a single operator to solve complex collaborative tasks. The perceptual information of all robots is fused into a joint virtual environment, which the operator can view from a top-down perspective to control the robots similar to puppets. We investigated differences between egocentric and allocentric multirobot teleoperation using both simulated and real robots. In the simulation study, study participants (n = 15) reported a higher sense of embodiment over robot bodies when using egocentric control, enabling them to perform more precise manipulation after initial training. Using allocentric control, participants achieved speed increases of 84% in a parallelizable locomotion task and 50% in a situational overview task compared with egocentric control. Participants reported improved system usability and overview with allocentric control, and no significant differences in sense of agency were measured. These results were validated in a real-world study with two physical robots (n = 6) and underscore the effectiveness of our allocentric teleoperation framework as a complementary approach to egocentric robotic teleoperation. Interfaces that support smooth switching between allocentric and egocentric control therefore enable operators to leverage the advantages of both paradigms depending on their specific task.
