Spiderbot: An Open-Source Energy-Efficient Hexapod with Passive Gravity Compensation
cs.RO, cs.SY, eess.SY
Submitted: 2026-09-22
Updated: 2026-09-22
Comments: 8 pages
Project page: https://erc-bpgc.github.io/SpiderBot
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: Hexapod robots can achieve static stability with fewer actuated joints than bipeds or quadrupeds, yet many platforms still use 3-DOF legs, increasing weight and continuous torque requirement with
Terminology
Abstract
Hexapod robots can achieve static stability with fewer actuated joints than bipeds or quadrupeds, yet many platforms still use 3-DOF legs, increasing weight and continuous torque requirement with limited gain in locomotion capability on flat, inclined and moderately rough terrains. We release Spiderbot, an open-source hexapod that uses a 4-bar linkage with a passive spring to mechanically support body weight, with a 2-DOF per-leg design that substantially reduces energy consumption. This mechanism substantially offloads gravitational torque during standing stance consuming only 1.5W (reduction of over 90% over the unsprung version and up to 96% over other similar hexapods). The passive spring compensation extends to payloads of up to 3.25kg with no additional torque requirements. The platform enables long-duration deployments on a modest battery budget and costs under 400, making it suitable for large-scale multi-agent experiments. We validate the locomotion capabilities of the platform with an RL policy trained in mjlab, including successful sim-to-real transfer, despite the complexity of the mechanism. The platform is evaluated on flat and rough terrains, slope up to 15 and step obstacles. We release all the CAD files, assembling instructions, and full training and deployment code along with the model checkpoints at https://erc-bpgc.github.io/SpiderBot/.
Sources
- Efficient Learning-Based Control of a Legged Robot in Lunar Gravity
- LAURON VI: A Six-Legged Robot for Dynamic Walking
- Control of a Hexapod Robot Considering Terrain Interaction
- Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
- mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning
- Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
- Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
- RMA: Rapid Motor Adaptation for Legged Robots
- Proximal Policy Optimization Algorithms
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