Environments with large terrain height variations present great challenges for legged robot locomotion. Drawing inspiration from fire ants' collective assembly behavior, we study strategies that can enable two ``connectable'' robots to collectively navigate over bumpy terrains with height variations larger than robot leg length. Each robot was designed to be extremely simple, with a cubical body and one rotary motor actuating four vertical peg legs that move in pairs. Two or more robots could physically connect to one another to enhance collective mobility. We performed locomotion experiments with a two-robot group, across an obstacle field filled with uniformly-distributed semi-spherical ``boulders''. Experimentally-measured robot speed suggested that the connection length between the robots has a significant effect on collective mobility: connection length C in [0.86, 0.9] robot unit body length (UBL) were able to produce sustainable movements across the obstacle field, whereas connection length C in [0.63, 0.84] and [0.92, 1.1] UBL resulted in low traversability. An energy landscape based model revealed the underlying mechanism of how connection length modulated collective mobility through the system's potential energy landscape, and informed adaptation strategies for the two-robot system to adapt their connection length for traversing obstacle fields with varying spatial frequencies. Our results demonstrated that by varying the connection configuration between the robots, the two-robot system could leverage mechanical intelligence to better utilize obstacle interaction forces and produce improved locomotion. Going forward, we envision that generalized principles of robot-environment coupling can inform design and control strategies for a large group of small robots to achieve ant-like collective environment negotiation.

Look at the images above. If I asked you to bring me a picnic blanket in the grassy field, would you be able to? If I asked you to bring over a cart full of food for a party, would you push the cart along the paved path or on the grass? Prior navigation approaches based purely on geometric reasoning incorrectly think that tall grass is an obstacle (above) and don't understand the difference between a smooth paved path and bumpy grass (below). While the answers to these questions may seem obvious, today's mobile robots would likely fail at these tasks: they would think the tall grass is the same as a concrete wall, and wouldn't know the difference between a smooth path and bumpy grass.

Look at the images above. If I asked you to bring me a picnic blanket in the grassy field, would you be able to? If I asked you to bring over a cart full of food for a party, would you push the cart along the paved path or on the grass? Prior navigation approaches based purely on geometric reasoning incorrectly think that tall grass is an obstacle (left) and don't understand the difference between a smooth paved path and bumpy grass (right). While the answers to these questions may seem obvious, today's mobile robots would likely fail at these tasks: they would think the tall grass is the same as a concrete wall, and wouldn't know the difference between a smooth path and bumpy grass.

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric view of the world can can be insufficient for many navigation problems. For example, a robot navigating based on geometry may avoid a field of tall grass because it believes it is untraversable, and will therefore fail to reach its desired goal. In this work, we investigate how to move beyond these purely geometric-based approaches using a method that learns about physical navigational affordances from experience. Our approach, which we call BADGR, is an end-to-end learning-based mobile robot navigation system that can be trained with self-supervised off-policy data gathered in real-world environments, without any simulation or human supervision. BADGR can navigate in real-world urban and off-road environments with geometrically distracting obstacles. It can also incorporate terrain preferences, generalize to novel environments, and continue to improve autonomously by gathering more data. Videos, code, and other supplemental material are available on our website https://sites.google.com/view/badgr