Socially Aware Motion Planning

[xiaoxianSun]

Hi!
This is an amazing work!I want to use the social norm to optimize the reward function.
I wonder if there is any possible that you could tell me the value of scalar penalty when you train the network! I didn't find the Specific value in this paper.
Thank you!

[mfe7]

Hi @xiaoxianSun , the IROS '18 paper didn't use the social reward, but in the IROS '17 paper the constant qn in Eqns 9-12 can be tuned according to the tradeoff described in the paper. Looking at some old code I believe we settled on 0.5*(some term based on how close the two agents are):

weight = 0.5; GAMMA = 0.97; DT_NORMAL = 0.5
d = np.linalg.norm(agent_i_pos - agent_j_pos)
v = agent_i_pref_speed
getting_close_penalty = GAMMA ** (d/DT_NORMAL) * (1.0 - GAMMA ** (-v/DT_NORMAL))
penalty = weight * getting_close_penalty

[xiaoxianSun]

Thanks for your kind reply.I am going to try it!