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/.
Figures & tables
Fig. 1: Spiderbot, the proposed open-source 12-DOF hexapod platform featuring spring-assisted four-bar legs for passive gravity compensation.
Fig. 2: Foot workspace visualisation
Fig. 3: 2-DOF vs 3-DOF leg workspace comparison
Fig. 4: Leg design and dimensions for our hexapod (measurements in mm).
Fig. 5: Free Body Diagram for link 1 (femur).
Symbol
Value
Description
m1
0.0608kg
Mass of Link 1 (L1)
m2
0.0125kg
Mass of Link 2 (L2)
m3
0.0124kg
Mass of Link 3 (L3)
mhb
2.59kg
Mass of base Body (B)
g
9.81m/s2
Gravitational acceleration
lL1
0.1463m
Length of L1
TABLE I: Known physical quantities of the robot
Fig. 6: Total Robot Torque Requirement (Femur Joint) vs Spring Stiffness (k)
Fig. 7: Additional torque requirement as a function of spring stiffness and payload mass for neutral stance configuration
Component
Qty.
Total Cost (USD)
ST3215 High-Torque Serial Servo Motor
6
126
SC15 Compact Serial Servo Motor
6
108
Arduino Uno Q Development Board
1
44
PLA Filament Spool (1 kg)
1
10
2-Cell 5200 mAh Li-Po Battery
1
30
RC Shock Spring 450 N/m
12
18
TABLE II: Bill of Materials (BOM) for Spiderbot
Fig. 8: Electronics architecture of Spiderbot featuring compute, sensors and actuation.
Parameter
Range
Operation
Foot friction coefficient
[0.3,1.2]
Absolute
Encoder bias (rad)
[−0.015,0.015]
Absolute
Base CoM offset (m)
[−0.025,0.025]xy,[−0.03,0.03]z
Additive
Base link mass (kg)
[−1.0,1.0]
Additive
Limb mass scale
[0.8,1.2]
Scaled
Motor zero offset (rad)
[−0.035,0.035]
Additive
TABLE III: Domain randomization parameters applied at training startup.
Terrain Type
Proportion
Parameters
Flat
0.20
Baseline flat ground.
Pyramid Stairs
0.20
Step height: 2–5 cm, step width: 0.5 m.
Inverted Stairs
0.20
Step height: 2–5 cm, step width: 0.5 m.
Pyramid Slope
0.10
Slope gradient: 0.05–0.35.
Inverted Slope
0.10
Slope gradient: 0.05–0.35, inverted.
Random Rough
0.10
Uniform noise height: 1.5–5 cm.
TABLE IV: Terrain types used during training.
Category
Reward Term
Weight
Task
Track linear velocity
+5.00
Track angular velocity
+2.00
Regulation
Upright orientation
+1.00
Joint pose
+1.00
Penalty
Joint position limits
−1.00
Action rate (L2)
−0.10
TABLE V: Active reward terms and their weights.
Fig. 9: Different environments in MuJoCo simulation. From left to right: flat ground, rough terrain, stairs
Energy-efficient robot locomotion studies how robot mechanics and control can reduce power use and cost of transport across terrains and movement modes.
Quadruped mammals coordinate sagittal spinal bending with axial extension and compression during dynamic locomotion. Yet most robotic quadrupeds use rigid trunks, passively compliant spines with fixed properties, or actively controlled spines that track prescribed trajectories. Whether actively regulated spinal compliance can support faster dynamic locomotion remains unclear. We present SPARC, a compact 1.26-kg, 3-DoF sagittal-plane spine that combines revolute and prismatic motion with independently tunable task-space stiffness and damping. A floating-base impedance controller renders the desired task-space compliance, and benchtop tests show that the fitted axial stiffness matches commanded values within 1.5%. We integrate SPARC into an 8-DoF quadruped and evaluate it across 97 bounding trials under three spine configurations: impedance-controlled SPARC, the same SPARC module held near a fixed pose using position control, and a lightweight rigid spine. Impedance-controlled SPARC reaches 1.029 m/s, compared with 0.769 m/s for position-controlled SPARC and 0.673 m/s for the rigid spine. Impedance-controlled SPARC reaches higher speeds with larger axial motion and greater mechanical power exchange, while at matched speed it has a higher electrical cost of transport than the rigid spine, revealing an energetic trade-off. Code and hardware are available at: https://github.com/YueWang996/sparc
Yue Wang, Yanran Xu
School of Electronic and Computer Science, University of Southampton, United Kingdom
Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation within a single optimization objective. We instead treat these functions through distinct mechanisms: rewards for task specification, constraints for operational limits, energy minimization for gait preference, and exteroceptive perception for adapting energy use to terrain difficulty. We show that these components jointly enable efficient, terrain-adaptive locomotion, and that removing each component exposes a distinct failure mode. Our formulation removes explicit gait priors (including air-time, contact-count, and foot-clearance targets) in favor of emergent behavior. Compared to a conventional complex-reward baseline, our formulation achieves comparable terrain traversal while reducing cost of transport by 56% and operational-limit violations by 96%. The resulting policies transfer zero-shot to a physical Unitree Go2 using LiDAR-based elevation mapping. Project website with videos: https://tinyurl.com/locomposition.
Loukas Kordos, Leonard T. Franz, Simon Rappenecker +4
Technical University of Munich. · University of Tübingen. · Hertie Institute for Clinical Brain Research & Center for Integrative Neuroscience. +1
We demonstrate a jumping robot that reaches high (7.6 m) and fast (190 ms stance time) jumps from a single long leg driven by a direct-drive transmission, without elastic energy storage. At 281 g, it achieves the highest jump yet reported for an electrically actuated, spring-free system. The leg uses a new fabric-wrap transmission that provides a variable mechanical advantage, keeping a small electric motor near its peak power output through the stroke while bracing the long, lightweight leg against buckling. A balancing module at the top of the leg uses small propellers to control the leg's orientation on the ground and in the air, where the long leg provides a large moment arm for the control torques. The robot is validated outdoors with vertical jumps, attitude control on the ground and in flight, and tilted jumps.
Gihyeok Na, Justin K. Yim
Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, IL 61801 USA