BAM
A tabular prediction model belonging to the GA²M family that constrains each pairwise shape function to an asymmetric bivariate Gaussian distribution. This constraint yields unimodal contour plots for each interaction term, making the learned pairwise effects easier to inspect visually and thereby improving the auditability of the model.
Timeline (last 90 days)
BAM's single-peak asymmetric bivariate Gaussian assumption may fail to capture certain real complex interaction patterns.
The paper's authors point out that strong auditability requires the shape functions themselves to be sufficiently simple, not merely plottable after the fact.
Simplifying shape functions does not sacrifice prediction accuracy, provided the right feature pairs are selected.
BAM (bivariate additive model) belongs to the GA²M family, and its core constraint is that every pairwise shape function is restricted to an asymmetric bivariate Gaussian distribution.
In BAM, the selection of feature pairs is done automatically during training, without the need for manual exhaustive combinations.
The paper conducted experiments on six tabular benchmark datasets, and the results show that BAM's prediction accuracy is comparable to standard GAM and close to the level of GA²M.
In the BAM experiments, a larger number of components generally yields higher accuracy but also increases the audit burden, providing practitioners with an adjustment knob.
BAM can systematically calibrate and model common servo motors (such as the Dynamixel series), covering parameters including torque-speed curves, control latency, gear backlash, and thermal characteristics.
BAM is an open-source actuator accurate modeling tool released by the Rhoban team, with the repository located at github.com/Rhoban/bam.
Physical parameters modeled by BAM include torque-velocity curves, control latency, gear backlash, thermal characteristics, and others, all of which are critical for sim-to-real transfer.
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All Facts (13)
BAM's single-peak asymmetric bivariate Gaussian assumption may fail to capture certain real complex interaction patterns.
50%UnverifiedThe paper's authors point out that strong auditability requires the shape functions themselves to be sufficiently simple, not merely plottable after the fact.
50%UnverifiedSimplifying shape functions does not sacrifice prediction accuracy, provided the right feature pairs are selected.
50%UnverifiedBAM (bivariate additive model) belongs to the GA²M family, and its core constraint is that every pairwise shape function is restricted to an asymmetric bivariate Gaussian distribution.
50%UnverifiedIn BAM, the selection of feature pairs is done automatically during training, without the need for manual exhaustive combinations.
50%UnverifiedThe paper conducted experiments on six tabular benchmark datasets, and the results show that BAM's prediction accuracy is comparable to standard GAM and close to the level of GA²M.
50%UnverifiedIn the BAM experiments, a larger number of components generally yields higher accuracy but also increases the audit burden, providing practitioners with an adjustment knob.
50%UnverifiedBAM can systematically calibrate and model common servo motors (such as the Dynamixel series), covering parameters including torque-speed curves, control latency, gear backlash, and thermal characteristics.
50%UnverifiedBAM is an open-source actuator accurate modeling tool released by the Rhoban team, with the repository located at github.com/Rhoban/bam.
50%UnverifiedPhysical parameters modeled by BAM include torque-velocity curves, control latency, gear backlash, thermal characteristics, and others, all of which are critical for sim-to-real transfer.
50%UnverifiedMicroduck's approach leans more toward the system identification route, precisely modeling actuators through the BAM tool while supplementing with moderate domain randomization to enhance robustness.
50%UnverifiedMicroduck's sim2real solution leans toward the System Identification approach, performing precise actuator modeling via the BAM tool while supplementing it with moderate Domain Randomization.
50%UnverifiedBAM is an open-source actuator precision modeling tool released by the Rhoban team that enables systematic calibration of servo motors, including parameters such as torque-velocity curves, control delay, gear backlash, and thermal characteristics.
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