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Robotics Today· June 30, 2020· 1:18:08

"Recent Progress on Atlas, the World’s Most Dynamic Humanoid Robot" - Scott Kuindersma

Scott Kuindersma, a research scientist at Boston Dynamics, presents Atlas research at Robotics Today, followed by a panel discussion with host Alberto Rodriguez, guest specialist Sangbae Kim, and audience members.

In this talk

  1. 1:59

    Atlas and Boston Dynamics

    Kuindersma describes Boston Dynamics’ goal of building robots with human or animal level performance in movement, perception, and decision making. He distinguishes Atlas as a research platform from products such as Spot, Handle, and Pick, and summarizes the hardware features that support Atlas’s strength and agility, including high power batteries, hydraulic systems, and lightweight components.

  2. 10:24

    Parkour as a test of dynamic control

    Atlas’s research goal is to improve dynamic whole body control, with parkour serving as a demanding test of agility, environmental response, and varied movement. Kuindersma outlines a system that creates motion templates offline through trajectory optimization, then uses perception and model predictive control (MPC) to adapt and execute them on the robot.

  3. 17:56

    From planned motions to robot behavior

    The optimization process first plans the robot’s momentum and then finds a full body motion that satisfies that plan and task specific constraints. Kuindersma shows examples including jumps onto platforms, hand supported landings, and failed flips caused by toe slip and actuator limits. Online MPC can adjust contact locations, timing, and forces when the robot’s real motion differs from its template.

  4. 28:43

    Perception and connected behaviors

    Depth cameras and plane fitting help Atlas identify nearby surfaces and plan contacts. MPC can retarget stored motions to objects in the robot’s actual surroundings, then blend consecutive jumps so the robot moves through a course without stopping. Adding single footed motions to the behavior library lets Atlas traverse obstacles with jogging steps as well as jumps.

  5. 42:43

    Research limits and next steps

    Kuindersma says Atlas is still building a foundation in agility, control, and state estimation; future work includes perception, autonomy, planning, learning, and interaction. In the panel, he discusses using data driven methods to complement model based control, balancing model detail against computational limits, and learning from repeated attempts. He advises students to get hands on robot experience and choose methods based on the problem.

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